Miscellaneous - Survey-Kit

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# Miscellaneous

## Serializable

Source code in `src\survey_kit\serializable.py`

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    class Serializable:
        _save_exclude_items = []
        _save_suffix = "serial"
        _registry = {}

        try:
            import numpy as np

            _BASE_TYPES = (int, float, str, bool, np.float64)
        except ImportError:
            _BASE_TYPES = (int, float, str, bool)

        def __init__(self):
            self.__fully_serializable__ = True

        def __init_subclass__(cls, **kwargs):
            """Automatically register subclasses when they're defined"""
            super().__init_subclass__(**kwargs)
            if hasattr(cls, "_save_suffix") and cls._save_suffix:
                Serializable._registry[cls._save_suffix] = cls
                logger.debug(f"Registered {cls.__name__} with suffix '{cls._save_suffix}'")

        @classmethod
        def _init_from_dict(
            cls, data: dict, init_kwargs: dict | None = None, **additional_kwargs
        ):
            init_keys = list(inspect.signature(cls.__init__).parameters.keys())
            init_keys.remove("self")
            if init_kwargs is None:
                init_kwargs = {}
            remaining_kwargs = {}
            for key, value in data.items():
                if key in init_keys:
                    init_kwargs[key] = value
                else:
                    remaining_kwargs[key] = value

            obj = cls(**init_kwargs, **additional_kwargs)

            for keyi, valuei in remaining_kwargs.items():
                if (keyi not in obj._reserved_keys()) and (
                    keyi not in obj._save_exclude_items
                ):
                    setattr(obj, keyi, valuei)

            return obj

        def __hash__(self):
            d_save = self.to_dict()
            if self.__fully_serializable__:
                save_caller = json
            else:
                save_caller = pickle

            return hash(save_caller.dumps(d_save))

        def save(self, path: str, quietly: bool = True) -> None:
            """
            Save a serializable object to

            Parameters
            ----------
            path : str, optional
                Path of object.  Can exclude the suffix.
            quietly: bool, optional
                No message to console/log
            """

            self.__serializable_n_dfs = 0

            d_save = self.to_dict()

            if type(path) is not str:
                path = str(path)
            if not path.endswith(f".{self._save_suffix}"):
                folder_path = f"{path}.{self._save_suffix}"
            else:
                folder_path = path

            #   logger.info(folder_path)

            if os.path.isdir(folder_path):
                logger.info("Removing existing directory " + folder_path)
                shutil.rmtree(folder_path)

            #   Make the path to save everything
            create_folders_if_needed([folder_path])
            #   os.makedirs(folder_path)

            #   Save the data
            dfs = d_save["__serialized_dfs__"]
            if len(dfs):
                self._save_dfs(folder_path=folder_path, dfs=dfs, quietly=quietly)

            if self.__fully_serializable__:
                suffix = "json"
                save_caller = json
                w = "w"
            else:
                suffix = "pkl"
                save_caller = pickle
                w = "wb"

            path_object = f"{folder_path}/{self._save_suffix}.{suffix}"
            with open(path_object, w) as f:
                save_caller.dump(d_save, f)

        def _save_dfs(self, folder_path: str, dfs: dict, quietly: bool = True) -> None:
            for valuei in dfs.values():
                dfi = valuei["df"]
                pathi = valuei["path"]

                path_save = os.path.normpath(f"{folder_path}/{pathi}")
                create_folders_if_needed([os.path.dirname(path_save)])

                dfi_nw = nw.from_native(dfi)
                d_metadata = dict(engine=nw.get_native_namespace(dfi_nw).__package__)
                SerializableDictionary(d_metadata).save(path_save)
                if isinstance(dfi_nw, nw.LazyFrame):
                    dfi_nw.sink_parquet(path_save)
                else:
                    dfi_nw.write_parquet(path_save)

                del valuei["df"]

        @classmethod
        def delete(cls, path: str):
            cls.load(path=path, delete=True, delete_only=True)

        @classmethod
        def load(
            cls,
            path: str = "",
            delete: bool = False,
            delete_only: bool = False,
            init_kwargs: dict | None = None,
            **df_kwargs,
        ) -> Serializable | None:
            """
            Load a serializable object from disk

            Parameters
            ----------
            path : str, optional
                Path of object.  Can exclude the suffix.
            delete : bool, optional
                Delete after load? The default is False.
            delete_only : bool, optional
                Don't load, just delete. The default is False.
            init_kwargs : dict, optional
                Arguments to pass to init function
            df_kwargs : dict, optional
                Arguments to pass to narwhals scan_parquet for dataframe loading
            Returns
            -------
            Serializable
            """

            if type(path) is not str:
                path = str(path)

            # If called on Serializable base class, auto-detect the subclass
            if cls == Serializable:
                # Find which suffix matches
                detected_class = None
                for suffix, registered_cls in Serializable._registry.items():
                    if path.endswith(f".{suffix}") or os.path.isdir(f"{path}.{suffix}"):
                        detected_class = registered_cls
                        break

                if detected_class is None:
                    message = f"Could not detect serializable type for path: {path}"
                    logger.error(message)
                    raise Exception(message)

                # Delegate to the correct class
                return detected_class.load(
                    path=path, delete=delete, delete_only=delete_only, **df_kwargs
                )

            if not path.endswith(f".{cls._save_suffix}"):
                folder_path = f"{path}.{cls._save_suffix}"
            else:
                folder_path = path

            if not delete_only:
                path_dict = os.path.normpath(f"{folder_path}/{cls._save_suffix}.json")
                path_found = os.path.isfile(path_dict)

                if path_found:
                    load_caller = json
                else:
                    path_dict = os.path.normpath(f"{folder_path}/{cls._save_suffix}.pkl")
                    load_caller = pickle
                    path_found = os.path.isfile(path_dict)

                if not path_found:
                    message = f"{folder_path} isn't a valid Serializable object"
                    logger.error(message)
                    raise Exception(message)

                with open(path_dict, "rb") as f:
                    d_loaded = load_caller.load(f)

                if len(d_loaded["__serialized_dfs__"]):
                    cls._load_dfs(
                        folder_path=folder_path,
                        dfs=d_loaded["__serialized_dfs__"],
                        delete=delete,
                        **df_kwargs,
                    )

            if delete or delete_only:
                if os.path.isdir(folder_path):
                    logger.info("Removing existing directory " + folder_path)
                    shutil.rmtree(folder_path)

            if delete_only:
                return None

            obj = cls.from_dict(d_loaded, init_kwargs=init_kwargs)
            return obj

        @classmethod
        def load_any(cls, path: str = "", delete: bool = False, delete_only: bool = False):
            """
            Pass the root path of a serializable object
                and this will figure out what it is (from the suffix)
                and call that object's loader function.

                For cases where multiple objects work, and I don't want to
                have to do if/else logic by suffix

            Parameters
            ----------
            path : str, optional
                Path of object.  Can exclude the suffix.
            delete : bool, optional
                Delete after load? The default is False.
            delete_only : bool, optional
                Don't load, just delete. The default is False.

            Returns
            -------
            Serializable object of any type

            """

            items = []

            for classi in items:
                suffixi = classi._save_suffix
                if path.endswith(suffixi) or os.path.isdir(f"{path}.{suffixi}"):
                    return classi.load(path=path, delete=delete, delete_only=delete_only)

        @classmethod
        def _load_dfs(
            cls, folder_path: str, dfs: dict, delete: bool = False, **df_kwargs
        ) -> None:
            #   Avoid circular import
            from .utilities.dataframe import NarwhalsType

            if df_kwargs is None:
                df_kwargs = {}
            for keyi, valuei in dfs.items():
                pathi = valuei["path"]

                path_load = os.path.normpath(f"{folder_path}/{pathi}")
                d_metadata = SerializableDictionary.load(path_load)
                df_kwargsi = df_kwargs.copy()
                if "backend" not in df_kwargsi:
                    df_kwargsi["backend"] = d_metadata["engine"]
                    backend = d_metadata["engine"]
                else:
                    backend = df_kwargsi["backend"]
                dfi = nw.scan_parquet(path_load, **df_kwargsi)
                if delete:
                    dfi = dfi.lazy().collect().lazy_backend(NarwhalsType(backend=backend))

                dfs[keyi]["df"] = dfi.to_native()

        def to_dict(self, dfs: dict | None = None, key_path: str = "") -> dict[str, object]:
            candidate_items = vars(self)

            self.__fully_serializable__ = True
            items = {}
            if dfs is None:
                add_dfs = True
                dfs = {}
            else:
                add_dfs = False

            for keyi, valuei in candidate_items.items():
                self._to_dict_item(
                    items=items, dfs=dfs, key_path=key_path, key=keyi, value=valuei
                )

            items["__fully_serializable__"] = self.__fully_serializable__
            items["__class__"] = self.__class__.__qualname__
            if add_dfs:
                items["__serialized_dfs__"] = dfs
            else:
                items["__serialized_dfs__"] = {}

            namespace = vars(inspect.getmodule(self))["__name__"]
            items["__namespace__"] = namespace

            #   logger.info(items)
            return items

        @classmethod
        def from_dict(
            cls,
            data: dict,
            init_kwargs: dict | None = None,
        ) -> object:
            data = cls._from_dict_unpack_item(data, dfs=None, init_kwargs=init_kwargs)

            return data

        @classmethod
        def _from_dict_unpack_item(
            cls,
            item: object,
            dfs: dict | None,
            init_kwargs: dict | None = None,
        ) -> object:
            if dfs is None:
                if "__serialized_dfs__" in item.keys():
                    dfs = item["__serialized_dfs__"]
                    del item["__serialized_dfs__"]
                else:
                    dfs = {}

            typei = type(item)

            if typei is dict:
                if "__enumeration__" in item.keys():
                    item = cls._from_dict_unpack_enum(item, dfs)
                elif "__pickled__" in item.keys():
                    item = cls._from_dict_unpack_pickled(item, dfs)
                # elif "__polars_expression__" in item.keys():
                #     item = cls._from_dict_unpack_polars_expression(item,
                #                                                    dfs)
                elif cls._is_serializable(item):
                    #   Unpack as a separate item
                    item = cls._from_dict_unpack_dict(item, dfs, init_kwargs=init_kwargs)

                    #   Then turn it into a class
                    _namespace = importlib.import_module(item["__namespace__"])
                    _class_name = item["__class__"]
                    _parent = _namespace
                    for classi in _class_name.split("."):
                        # #   Fix for any refactor class name changes
                        # d_refactor = {"MultipleImputationStats":"MultipleImputation"}

                        # classi = d_refactor.get(classi,
                        #                         classi)

                        _class = getattr(_parent, classi)
                        _parent = _class

                    #   _class = getattr(_namespace, item['__class__'])

                    init_sig = inspect.signature(_class._init_from_dict)
                    if "init_kwargs" in init_sig.parameters.keys():
                        item = _class._init_from_dict(item, init_kwargs=init_kwargs)
                    else:
                        if init_kwargs is None:
                            init_kwargs = {}
                        item = _class._init_from_dict(item, **init_kwargs)

                else:
                    item = cls._from_dict_unpack_dict(item, dfs)
            elif typei is list:
                item = cls._from_dict_unpack_list(item, dfs)
            elif typei is str and item.startswith("__serialized_df_"):
                item = cls._from_dict_assign_df(item, dfs)
            elif typei in [int, float, str, bool]:
                #   Do nothing - don't unpack
                pass

            return item

        @classmethod
        def _from_dict_unpack_dict(
            cls, item: dict, dfs: dict, init_kwargs: dict | None = None
        ) -> object:
            for key, value in item.items():
                if key not in cls._reserved_keys():
                    item[key] = cls._from_dict_unpack_item(value, dfs, init_kwargs)

            return item

        @classmethod
        def _from_dict_unpack_list(cls, item: list, dfs: dict):
            for i in range(0, len(item)):
                item[i] = cls._from_dict_unpack_item(item[i], dfs)

            return item

        @classmethod
        def _from_dict_unpack_enum(cls, item: dict, dfs: dict):
            #   Then turn it into a class
            _namespace = importlib.import_module(item["__enum_namespace__"])
            _class_name = item["__enum_class__"]
            _parent = _namespace
            for classi in _class_name.split("."):
                _class = getattr(_parent, classi)
                _parent = _class
            value = item["__enumeration__"]

            return _class(value)

        @classmethod
        def _from_dict_unpack_polars_expression(cls, item: dict, dfs: dict):
            try:
                import polars as pl
            except ImportError:
                raise ImportError("Polars is required for unpacking a polars Expression. ")

            return pl.Expr.deserialize(io.StringIO(item["__polars_expression__"]))

        @classmethod
        def _from_dict_unpack_pickled(cls, item: dict, dfs: dict):
            pickled_data = bytes(item["__pickled__"])
            buffer = io.BytesIO(pickled_data)
            try:
                return pickle.load(buffer)
            except:
                logger.info("Pickled item failed to load")

        @classmethod
        def _from_dict_assign_df(cls, item: str, dfs: dict):
            return dfs[item]["df"]

        def _to_dict_item(
            self,
            items: dict[str, object],
            dfs: dict[str, dict],
            key_path: str,
            key: str,
            value: object,
        ) -> None:
            # logger.info(key)
            # logger.info(value)
            # logger.info(items)
            # logger.info(key_path)

            if (key not in self._reserved_keys()) and (key not in self._save_exclude_items):
                typei = type(value)

                if typei is list:
                    d_item = []

                    for i in range(0, len(value)):
                        self._to_dict_item(
                            items=d_item,
                            dfs=dfs,
                            key_path=f"{key_path}/{key}/{i}",
                            key=i,
                            value=value[i],
                        )

                    self._add_to_items(key=key, value=d_item, items=items)
                elif typei is dict:
                    d_item = {}

                    for ki, vi in value.items():
                        self._to_dict_item(
                            items=d_item,
                            dfs=dfs,
                            key_path=f"{key_path}/{key}",
                            key=ki,
                            value=vi,
                        )

                    self._add_to_items(key=key, value=d_item, items=items)
                elif is_narwhals_compatible(value):
                    n_dfs = len(dfs.keys())

                    df_name = f"__serialized_df_{n_dfs}"
                    dfs[df_name] = {
                        "df": value,
                        "path": f"{key_path}/{key}/{df_name}.parquet",
                    }

                    self._add_to_items(key=key, value=df_name, items=items)
                elif typei in self._BASE_TYPES:
                    self._add_to_items(key=key, value=value, items=items)
                # elif isinstance(value,pl.Expr):
                #     self._add_to_items(key=key,
                #                        value=self._add_to_items_polars_expression(value),
                #                        items=items)
                elif isinstance(value, Serializable):
                    value_add = value.to_dict(dfs=dfs, key_path=key_path)
                    self.__fully_serializable__ = (
                        self.__fully_serializable__ and value.__fully_serializable__
                    )
                    self._add_to_items(key=key, value=value_add, items=items)
                elif isinstance(value, Enum):
                    value_add = self._add_to_items_enumeration(value)
                    self._add_to_items(key=key, value=value_add, items=items)
                elif value is None:
                    self._add_to_items(key=key, value=value, items=items)
                else:
                    # logger.info(f"{key} not serializable")
                    # logger.info(value)
                    #   self.__fully_serializable__ = False
                    self._add_to_items(
                        key=key, value=self._add_to_items_pickled(value), items=items
                    )

        def _add_to_items_enumeration(self, value: Enum) -> dict:
            namespace = value.__module__
            # vars(inspect.getmodule(self))["__name__"]

            # logger.info({
            #     "__enumeration__": value.value,
            #     "__enum_class__": value.__class__.__qualname__,
            #     "__enum_namespace__": namespace,
            # })
            # logger.info("")
            return {
                "__enumeration__": value.value,
                "__enum_class__": value.__class__.__qualname__,
                "__enum_namespace__": namespace,
            }

        def _add_to_items_polars_expression(self, value: pl.Expr) -> dict:
            return {"__polars_expression__": value.meta.serialize()}

        def _add_to_items_pickled(self, value: pl.Expr) -> dict:
            buffer = io.BytesIO()
            pickle.dump(value, buffer)
            value_save = list(buffer.getvalue())
            return {"__pickled__": value_save}

        @staticmethod
        def _add_to_items(key: str, value: object, items: dict | list):
            if type(items) is dict:
                items[key] = value
            elif type(items) is list:
                items.append(value)

        @staticmethod
        def _reserved_keys() -> list[str]:
            return [
                "__class__",
                "__namespace__",
                "__fully_serializable__",
                "__dfs__",
                "__dfpath__",
            ]

        @staticmethod
        def _is_serializable(data: dict):
            return (
                ("__fully_serializable__" in data.keys())
                and ("__class__" in data.keys())
                and ("__namespace__" in data.keys())
            )

### load `classmethod`

    load(
        path: str = "",
        delete: bool = False,
        delete_only: bool = False,
        init_kwargs: dict | None = None,
        **df_kwargs,
    ) -> Serializable | None

Load a serializable object from disk

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`path` | `str` | Path of object. Can exclude the suffix. | `''`
`delete` | `bool` | Delete after load? The default is False. | `False`
`delete_only` | `bool` | Don't load, just delete. The default is False. | `False`
`init_kwargs` | `dict` | Arguments to pass to init function | `None`
`df_kwargs` | `dict` | Arguments to pass to narwhals scan_parquet for dataframe loading | `{}`

Returns:

Type | Description
--- | ---
`Serializable` |

Source code in `src\survey_kit\serializable.py`

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    @classmethod
    def load(
        cls,
        path: str = "",
        delete: bool = False,
        delete_only: bool = False,
        init_kwargs: dict | None = None,
        **df_kwargs,
    ) -> Serializable | None:
        """
        Load a serializable object from disk

        Parameters
        ----------
        path : str, optional
            Path of object.  Can exclude the suffix.
        delete : bool, optional
            Delete after load? The default is False.
        delete_only : bool, optional
            Don't load, just delete. The default is False.
        init_kwargs : dict, optional
            Arguments to pass to init function
        df_kwargs : dict, optional
            Arguments to pass to narwhals scan_parquet for dataframe loading
        Returns
        -------
        Serializable
        """

        if type(path) is not str:
            path = str(path)

        # If called on Serializable base class, auto-detect the subclass
        if cls == Serializable:
            # Find which suffix matches
            detected_class = None
            for suffix, registered_cls in Serializable._registry.items():
                if path.endswith(f".{suffix}") or os.path.isdir(f"{path}.{suffix}"):
                    detected_class = registered_cls
                    break

            if detected_class is None:
                message = f"Could not detect serializable type for path: {path}"
                logger.error(message)
                raise Exception(message)

            # Delegate to the correct class
            return detected_class.load(
                path=path, delete=delete, delete_only=delete_only, **df_kwargs
            )

        if not path.endswith(f".{cls._save_suffix}"):
            folder_path = f"{path}.{cls._save_suffix}"
        else:
            folder_path = path

        if not delete_only:
            path_dict = os.path.normpath(f"{folder_path}/{cls._save_suffix}.json")
            path_found = os.path.isfile(path_dict)

            if path_found:
                load_caller = json
            else:
                path_dict = os.path.normpath(f"{folder_path}/{cls._save_suffix}.pkl")
                load_caller = pickle
                path_found = os.path.isfile(path_dict)

            if not path_found:
                message = f"{folder_path} isn't a valid Serializable object"
                logger.error(message)
                raise Exception(message)

            with open(path_dict, "rb") as f:
                d_loaded = load_caller.load(f)

            if len(d_loaded["__serialized_dfs__"]):
                cls._load_dfs(
                    folder_path=folder_path,
                    dfs=d_loaded["__serialized_dfs__"],
                    delete=delete,
                    **df_kwargs,
                )

        if delete or delete_only:
            if os.path.isdir(folder_path):
                logger.info("Removing existing directory " + folder_path)
                shutil.rmtree(folder_path)

        if delete_only:
            return None

        obj = cls.from_dict(d_loaded, init_kwargs=init_kwargs)
        return obj

### load_any `classmethod`

    load_any(
        path: str = "",
        delete: bool = False,
        delete_only: bool = False,
    )

Pass the root path of a serializable object and this will figure out what it is (from the suffix) and call that object's loader function.

    For cases where multiple objects work, and I don't want to
    have to do if/else logic by suffix

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`path` | `str` | Path of object. Can exclude the suffix. | `''`
`delete` | `bool` | Delete after load? The default is False. | `False`
`delete_only` | `bool` | Don't load, just delete. The default is False. | `False`

Returns:

Type | Description
--- | ---
`Serializable object of any type` |

Source code in `src\survey_kit\serializable.py`

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    @classmethod
    def load_any(cls, path: str = "", delete: bool = False, delete_only: bool = False):
        """
        Pass the root path of a serializable object
            and this will figure out what it is (from the suffix)
            and call that object's loader function.

            For cases where multiple objects work, and I don't want to
            have to do if/else logic by suffix

        Parameters
        ----------
        path : str, optional
            Path of object.  Can exclude the suffix.
        delete : bool, optional
            Delete after load? The default is False.
        delete_only : bool, optional
            Don't load, just delete. The default is False.

        Returns
        -------
        Serializable object of any type

        """

        items = []

        for classi in items:
            suffixi = classi._save_suffix
            if path.endswith(suffixi) or os.path.isdir(f"{path}.{suffixi}"):
                return classi.load(path=path, delete=delete, delete_only=delete_only)

### save

    save(path: str, quietly: bool = True) -> None

Save a serializable object to

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`path` | `str` | Path of object. Can exclude the suffix. | required
`quietly` | `bool` | No message to console/log | `True`

Source code in `src\survey_kit\serializable.py`

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    def save(self, path: str, quietly: bool = True) -> None:
        """
        Save a serializable object to

        Parameters
        ----------
        path : str, optional
            Path of object.  Can exclude the suffix.
        quietly: bool, optional
            No message to console/log
        """

        self.__serializable_n_dfs = 0

        d_save = self.to_dict()

        if type(path) is not str:
            path = str(path)
        if not path.endswith(f".{self._save_suffix}"):
            folder_path = f"{path}.{self._save_suffix}"
        else:
            folder_path = path

        #   logger.info(folder_path)

        if os.path.isdir(folder_path):
            logger.info("Removing existing directory " + folder_path)
            shutil.rmtree(folder_path)

        #   Make the path to save everything
        create_folders_if_needed([folder_path])
        #   os.makedirs(folder_path)

        #   Save the data
        dfs = d_save["__serialized_dfs__"]
        if len(dfs):
            self._save_dfs(folder_path=folder_path, dfs=dfs, quietly=quietly)

        if self.__fully_serializable__:
            suffix = "json"
            save_caller = json
            w = "w"
        else:
            suffix = "pkl"
            save_caller = pickle
            w = "wb"

        path_object = f"{folder_path}/{self._save_suffix}.{suffix}"
        with open(path_object, w) as f:
            save_caller.dump(d_save, f)

## DataFrameList

Bases: `Serializable`

A light wrapper around Lazy/DataFrames that can be used like a list, mostly, (append, extend, +) and also can be used like the underlying LazyFrame or DataFrame where a narwhals operation is applied to all items in the list.

Examples:

    >>> df_list = DataFrameList([df1, df2])
    >>> df_list = df_list.filter(nw.col("a") == 1)

This would apply the filter to df1 and df2 and return a DataFrameList with the filtered data.

Source code in `src\survey_kit\utilities\dataframe_list.py`

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    class DataFrameList(Serializable):
        """
        A light wrapper around Lazy/DataFrames
        that can be used like a list, mostly, (append, extend, +)
        and also can be used like the underlying LazyFrame or DataFrame
        where a narwhals operation is applied to all items in the list.

        Examples
        --------
        >>> df_list = DataFrameList([df1, df2])
        >>> df_list = df_list.filter(nw.col("a") == 1)

        This would apply the filter to df1 and df2 and return a DataFrameList with
        the filtered data.

        """

        _save_suffix = "df_list"

        def __init__(self, df_list: list[IntoFrameT]):
            self._df_list = df_list

        def __getitem__(self, index):
            return self._df_list[index]

        def __setitem__(self, index, value):
            self._df_list[index] = value

        def __len__(self):
            return len(self._df_list)

        def append(self, df: IntoFrameT):
            self._df_list.append(df)

        def extend(self, iterable: DataFrameList | list[IntoFrameT]):
            if type(iterable) is DataFrameList:
                self._df_list.extend(iterable._df_list)
            else:
                self._df_list.extend(iterable)

        def __add__(self, other: DataFrameList | list[IntoFrameT]):
            if type(other) is DataFrameList:
                self._df_list = self._df_list + other._df_list
                return self
            else:
                self._df_list = self._df_list + other
                return self

        def __repr__(self):
            #   Mimic how lists of polars tables are displayed
            return ",\n".join([dfi.__repr__() for dfi in self._df_list])

        def __iter__(self):
            return iter(self._df_list)

        def __getattr__(self, attr):
            """
            Delegate attribute to polars LazyFrame | DataFrame
                Determines lazy/data based on df_list object 0

            """

            if hasattr(nw.from_native(self._df_list[0]), attr):
                this_attr = getattr(nw.from_native(self._df_list[0]), attr)
                if callable(this_attr):

                    def wrapper(*args, **kwargs):
                        output = [
                            getattr(nw.from_native(dfi), attr)(*args, **kwargs)
                            for dfi in self._df_list
                        ]
                        if isinstance(output[0], (nw.LazyFrame, nw.DataFrame)):
                            return DataFrameList([dfi.to_native() for dfi in output])
                        else:
                            return DataFrameList([itemi for itemi in output])

                    return wrapper
                else:
                    output = [getattr(nw.from_native(dfi), attr) for dfi in self._df_list]
                    if isinstance(output[0], (nw.LazyFrame, nw.DataFrame)):
                        return DataFrameList([dfi.to_native() for dfi in output])
                    else:
                        return DataFrameList([itemi for itemi in output])

            else:
                raise AttributeError(
                    f"{type(nw.from_native(self._df_list[0]))} has no attribute '{attr}'"
                )

        def join_to_list(
            self,
            df_join: list[IntoFrameT],
            on: list[str],
            how: str,
            suffixes: list[str] | None = None,
            prefixes: list[str] | None = None,
        ) -> DataFrameList:
            prefixes = list_input(prefixes)
            suffixes = list_input(suffixes)

            if len(prefixes) and len(prefixes) == len(df_join):
                prefixes = [""] + prefixes
            if len(suffixes) and len(suffixes) == len(df_join):
                suffixes = [""] + suffixes

            for i in range(0, len(self._df_list)):
                self._df_list[i] = join_list(
                    [self._df_list[i]] + df_join,
                    on=on,
                    how=how,
                    prefixes=prefixes,
                    suffixes=suffixes,
                )
            return self

        def append_list(self, df_append: list[IntoFrameT]) -> DataFrameList:
            for i in range(0, len(self._df_list)):
                self._df_list[i] = concat_wrapper(
                    df_list=[self._df_list[i]] + df_append, how="diagonal"
                )

            return self

        def calculate_stats(
            self,
            statistics: list[Statistics] | Statistics | None = None,
            weight: str = "",
            scale_wgts_to: float = 0.0,
            summarize_by: dict[str, list[str]] | None = None,
            display: bool = True,
            display_all_vars: bool = True,
            display_max_vars: int = 20,
            round_output: bool | int = True,
        ) -> DataFrameList:
            def stats_for_one(df: IntoFrameT):
                sc = StatCalculator(
                    df=df,
                    statistics=statistics,
                    weight=weight,
                    scale_wgts_to=scale_wgts_to,
                    summarize_by=summarize_by,
                    display=display,
                    display_all_vars=display_all_vars,
                    display_max_vars=display_max_vars,
                    round_output=round_output,
                )

                return sc.df_estimates

            return self.pipe(stats_for_one)

        def calculate_stats_average(
            self,
            statistics: list[Statistics] | Statistics | None = None,
            weight: str = "",
            scale_wgts_to: float = 0.0,
            summarize_by: dict[str, list[str]] | None = None,
            display: bool = True,
            display_all_vars: bool = True,
            display_max_vars: int = 20,
            round_output: bool | int = True,
        ) -> StatCalculator:
            df_list = self.calculate_stats(
                statistics=statistics,
                weight=weight,
                scale_wgts_to=scale_wgts_to,
                summarize_by=summarize_by,
                display=False,
                display_all_vars=display_all_vars,
                display_max_vars=display_max_vars,
                round_output=round_output,
            )

            sc = StatCalculator(
                df=self[0],
                statistics=statistics,
                weight=weight,
                scale_wgts_to=scale_wgts_to,
                summarize_by=summarize_by,
                display=False,
                display_all_vars=display_all_vars,
                display_max_vars=display_max_vars,
                round_output=round_output,
                calculate=False,
            )
            sc.df_estimates = df_list.average()

            if display:
                sc.print()

            return sc

        def average(self, order_by: list[str] | str | None = None) -> IntoFrameT:
            index = "__df_average_row_index__"
            order_by = list_input(order_by)
            if len(order_by) == 0:
                df_list = [
                    nw.from_native(dfi).lazy().collect().with_row_index(name=index)
                    for dfi in self._df_list
                ]
            else:
                df_list = [
                    nw.from_native(dfi).lazy().with_row_index(name=index, order_by=order_by)
                    for dfi in self._df_list
                ]
            df = concat_wrapper(df_list, how="diagonal")

            all_cols = df.collect_schema().names()

            order_by_full = order_by + [index]
            cols_numeric = (
                df.select(cs.numeric() | cs.boolean())
                .drop(order_by + [index])
                .collect_schema()
                .names()
            )
            cols_first = columns_from_list(
                df, columns="*", exclude=cols_numeric + order_by_full
            )

            with_agg = []
            if len(cols_first):
                with_agg.append(nw.col(cols_first).first())
            with_agg.append(nw.col(cols_numeric).mean())
            df_out = nw.from_native(df).lazy().group_by(order_by_full).agg(with_agg)

            return df_out.select(all_cols).sort(order_by_full).drop(index).to_native()

## compress_df

    compress_df(
        df: IntoFrameT,
        cols: list[str] | str | None = None,
        check_string: bool = False,
        check_string_only: bool = False,
        cast_all_null_to_boolean: bool = True,
        check_date_time: bool = True,
        no_boolean: bool = False,
    ) -> IntoFrameT

Optimize DataFrame by downcasting numeric types to smallest possible representation.

Analyzes numeric columns and casts them to the smallest data type that can accommodate all values, reducing memory usage and file sizes. Has some optional parameters to handle figuring out the optimal compress for stata

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`df` | `IntoFrameT` | Input data | required
`cols` | `list[str]` | Specific columns to compress (default: all) | `None`
`check_string` | `bool` | Attempt to convert string columns to numeric | `False`
`check_string_only` | `bool` | Only check string conversions | `False`
`cast_all_null_to_boolean` | `bool` | Cast all-null columns to boolean | `True`
`check_date_time` | `bool` | Optimize datetime columns | `True`
`no_boolean` | `bool` | Skip boolean type casting (and leave as int8) | `False`

Returns:

Type | Description
--- | ---
`IntoFrameT` | Compressed DataFrame with optimized data types

Examples:

Basic compression:

    >>> compressed_df = compress_df(df)

String to numeric conversion:

    >>> compressed_df = compress_df(df, check_string=True)

Notes

Automatically detects the smallest integer type that can hold all values in each column, considering ranges like Int8 (-128 to 127), Int16, etc.

Source code in `src\survey_kit\utilities\compress.py`

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    def compress_df(
        df: IntoFrameT,
        cols: list[str] | str | None = None,
        check_string: bool = False,
        check_string_only: bool = False,
        cast_all_null_to_boolean: bool = True,
        check_date_time: bool = True,
        no_boolean: bool = False,
    ) -> IntoFrameT:
        """
        Optimize DataFrame by downcasting numeric types to smallest possible representation.

        Analyzes numeric columns and casts them to the smallest data type that can
        accommodate all values, reducing memory usage and file sizes.
        Has some optional parameters to handle figuring out the optimal compress
        for stata

        Parameters
        ----------
        df : IntoFrameT
            Input data
        cols : list[str], optional
            Specific columns to compress (default: all)
        check_string : bool
            Attempt to convert string columns to numeric
        check_string_only : bool
            Only check string conversions
        cast_all_null_to_boolean : bool
            Cast all-null columns to boolean
        check_date_time : bool
            Optimize datetime columns
        no_boolean : bool
            Skip boolean type casting (and leave as int8)

        Returns
        -------
        IntoFrameT
            Compressed DataFrame with optimized data types

        Examples
        --------
        Basic compression:

        >>> compressed_df = compress_df(df)

        String to numeric conversion:

        >>> compressed_df = compress_df(df, check_string=True)

        Notes
        -----
        Automatically detects the smallest integer type that can hold all values
        in each column, considering ranges like Int8 (-128 to 127), Int16, etc.
        """

        nw_type = NarwhalsType(df)
        df = nw_type.to_polars()

        if check_date_time:
            df = _compress_datetime(df)

        #   Convert numerics
        intlist = {}
        if not no_boolean:
            intlist[pl.Boolean] = [0, 1]
        intlist[pl.Int8] = [-(2**7), 2**7 - 1]
        intlist[pl.Int16] = [-(2**15), 2**15 - 1]
        intlist[pl.Int32] = [-(2**31), 2**31 - 1]
        intlist[pl.Int64] = [-(2**63), 2**63 - 1]

        if cols is None:
            cols = df.lazy().collect_schema().names()

        for columni in cols:
            cast_complete = False
            plType = df.lazy().collect_schema()[columni]

            df_col = df.select(pl.col(columni))

            check_integers = False
            check_float32 = False

            maxValue = None
            minValue = None

            if check_string and (plType == pl.Utf8 or plType == pl.String):
                numeric_string = False
                try:
                    df_col = (
                        df_col.select(
                            pl.col(columni).str.strip_chars().cast(pl.Float64, strict=True)
                        )
                        .lazy()
                        .collect()
                    )
                    numeric_string = True
                except:
                    pass

                if numeric_string:
                    plType = pl.Float64
                    df = df.with_columns(df_col[columni].cast(plType).alias(columni))

            if plType == pl.Float64:
                check_integers = True
                check_float32 = False

                plType_intsize = 65
            elif plType == pl.Float32:
                check_integers = True
                check_float32 = False

                plType_intsize = 65

            elif plType == pl.Int64 or plType == pl.UInt64:
                check_integers = True

                plType_intsize = 64
            elif plType == pl.Int32 or plType == pl.UInt32:
                check_integers = True

                plType_intsize = 32
            elif plType == pl.Int16 or plType == pl.UInt16:
                check_integers = True

                plType_intsize = 16
            elif plType == pl.Int8 or plType == pl.UInt8:
                check_integers = True

                plType_intsize = 8

            #   First check integers
            if check_integers and not check_string_only:
                df_col = df_col.lazy().collect()

                dfCastCheck = df_col.filter(pl.col(columni).is_not_null())

                if dfCastCheck.height == 0 and df_col.height != 0:
                    #   All missing, cast to bool (for later combinations to ignore)?
                    if cast_all_null_to_boolean:
                        try:
                            #   Try casting on the non-null values
                            dfCastCheck = dfCastCheck.select(
                                pl.col(columni).cast(pl.Boolean, strict=True)
                            )
                            dfCastCheck = None
                            #   Worked - then we're good to do on all of them
                            dfcast = df_col.select(
                                pl.col(columni).cast(pl.Boolean, strict=True)
                            )
                            # logger.info("     Cast " + columni + " as " + str(inti))
                            cast_complete = True
                        except:
                            pass
                            #   logger.warning("     Cannot cast " + columni + " as " + str(pl.Boolean))
                else:
                    #   All integers?
                    if plType == pl.Float32 or plType == pl.Float64:
                        bAllIntegers = (
                            dfCastCheck.with_columns(pl.col(columni).mod(1) == 0).sum()[
                                0, 0
                            ]
                            == dfCastCheck.height
                        )
                    else:
                        bAllIntegers = True

                    #   Only downcast to an integer if all are integers
                    if bAllIntegers:
                        maxValue = dfCastCheck.max().row(0)[0]
                        minValue = dfCastCheck.min().row(0)[0]

                        if minValue is not None:
                            for inti in intlist:
                                if inti == pl.Boolean:
                                    intSize = 1
                                else:
                                    intSize = int(str(inti).replace("Int", ""))

                                if plType_intsize > intSize:
                                    #   logger.info(intlist[inti])
                                    lowerbound = intlist[inti][0]
                                    upperbound = intlist[inti][1]
                                    #   logger.info(lowerbound)
                                    #   logger.info(upperbound)

                                    if maxValue <= upperbound and minValue >= lowerbound:
                                        #   logger.info("in range for " + str(inti))

                                        try:
                                            #   Try casting on the non-null values
                                            dfCastCheck = dfCastCheck.select(
                                                pl.col(columni).cast(inti, strict=True)
                                            )
                                            dfCastCheck = None
                                            #   Worked - then we're good to do on all of them
                                            dfcast = df_col.select(
                                                pl.col(columni).cast(inti, strict=True)
                                            )
                                            # logger.info("     Cast " + columni + " as " + str(inti))
                                            cast_complete = True

                                        except:
                                            logger.warning(
                                                "     Cannot cast "
                                                + columni
                                                + " as "
                                                + str(inti)
                                            )

                                        if cast_complete:
                                            break

            if not cast_complete and check_float32 and minValue is not None:
                df_col = df_col.lazy().collect()
                try:
                    dfcast = df_col.select(pl.col(columni).cast(pl.Float32, strict=True))
                    # logger.info("     Cast " + columni + " as " + str(pl.Float32))
                    cast_complete = True
                except:
                    logger.warning("     Cannot cast " + columni + " as " + str(pl.Float32))

            if cast_complete:
                df = df.with_columns(dfcast[columni].alias(columni))

        df = nw_type.from_polars(df)

        return NarwhalsType.return_df(df, nw_type)

## Config

Global configuration for survey-kit.

Config manages package-wide settings including paths, CPU limits, memory settings, and environment variables. Settings can be configured via environment variables or by directly setting attributes on the config instance.

The config instance is typically accessed via:

        from survey_kit import config
        config.data_root = "/path/to/data"
        config.cpus = 8

Attributes:

Name | Type | Description
--- | --- | ---
`code_root` | `str` | Root directory for code files. Set via environment variable `_survey_kit_code_root_` or directly. Default is "".
`data_root` | `str` | Root directory for data files. Set via environment variable `_survey_kit_data_root_` or directly. Default is "".
`cpus` | `int` | Number of CPUs to use for parallel operations. Automatically sets thread limits for Polars, OpenBLAS, MKL, etc. when changed. Set via `_survey_kit_n_cpus_` or directly. Default is os.cpu_count().
`path_temp_files` | `str` | Directory for temporary files. Set via `_survey_kit_path_temp_files_` or directly. Default is {data_root}/temp_files.
`ram` | `int` | Total available RAM in bytes. Set via `_survey_kit_ram_`. Default is system total memory.
`mem_in_gb` | `int` | Available memory in gigabytes (read-only).
`mem_in_mb` | `int` | Available memory in megabytes (read-only).
`mem_in_kb` | `int` | Available memory in kilobytes (read-only).

Examples:

Basic configuration:

    >>> from survey_kit import config
    >>> config.data_root = "/projects/data/myproject"
    >>> config.cpus = 16
    >>> print(config.path_temp_files)
    '/projects/data/myproject/temp_files'

Using environment variables:

        export _survey_kit_data_root_="/projects/data/myproject"
        export _survey_kit_n_cpus_=16

Memory information:

    >>> print(f"Available RAM: {config.mem_in_gb} GB")
    >>> print(f"Available RAM: {config.mem_in_mb} MB")

Temporary files:

    >>> temp_path = config.path_temp_with_random(as_parquet=True)
    >>> print(temp_path)
    '/projects/data/myproject/temp_files/abc123xyz.parquet'

Notes

Setting `cpus` automatically updates thread limits for multiple libraries: - POLARS_MAX_THREADS - OMP_NUM_THREADS - NUMEXPR_NUM_THREADS - MKL_NUM_THREADS - OPENBLAS_NUM_THREADS

Source code in `src\survey_kit\orchestration\config.py`

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    class Config:
        """
        Global configuration for survey-kit.

        Config manages package-wide settings including paths, CPU limits, memory settings,
        and environment variables. Settings can be configured via environment variables
        or by directly setting attributes on the config instance.

        The config instance is typically accessed via:
        ```python
            from survey_kit import config
            config.data_root = "/path/to/data"
            config.cpus = 8
        ```

        Attributes
        ----------
        code_root : str
            Root directory for code files. Set via environment variable
            `_survey_kit_code_root_` or directly. Default is "".
        data_root : str
            Root directory for data files. Set via environment variable
            `_survey_kit_data_root_` or directly. Default is "".
        cpus : int
            Number of CPUs to use for parallel operations. Automatically sets
            thread limits for Polars, OpenBLAS, MKL, etc. when changed.
            Set via `_survey_kit_n_cpus_` or directly. Default is os.cpu_count().
        path_temp_files : str
            Directory for temporary files. Set via `_survey_kit_path_temp_files_`
            or directly. Default is {data_root}/temp_files.
        ram : int
            Total available RAM in bytes. Set via `_survey_kit_ram_`.
            Default is system total memory.
        mem_in_gb : int
            Available memory in gigabytes (read-only).
        mem_in_mb : int
            Available memory in megabytes (read-only).
        mem_in_kb : int
            Available memory in kilobytes (read-only).
        Examples
        --------
        Basic configuration:

        >>> from survey_kit import config
        >>> config.data_root = "/projects/data/myproject"
        >>> config.cpus = 16
        >>> print(config.path_temp_files)
        '/projects/data/myproject/temp_files'

        Using environment variables:
        ```bash
            export _survey_kit_data_root_="/projects/data/myproject"
            export _survey_kit_n_cpus_=16
        ```

        Memory information:

        >>> print(f"Available RAM: {config.mem_in_gb} GB")
        >>> print(f"Available RAM: {config.mem_in_mb} MB")

        Temporary files:

        >>> temp_path = config.path_temp_with_random(as_parquet=True)
        >>> print(temp_path)
        '/projects/data/myproject/temp_files/abc123xyz.parquet'

        Notes
        -----
        Setting `cpus` automatically updates thread limits for multiple libraries:
        - POLARS_MAX_THREADS
        - OMP_NUM_THREADS
        - NUMEXPR_NUM_THREADS
        - MKL_NUM_THREADS
        - OPENBLAS_NUM_THREADS

        """

        # parameter_files : dict
        #     Dictionary of parameter file paths. Set via `_survey_kit_parameter_files_`.
        #     Default is {}.
        # pbs_log_path : str
        #     Path for PBS job logs. Set via `_survey_kit_pbs_log_path_`.
        #     Default is "".
        # versions : list
        #     (IGNORE - INTENDED FOR FUTURE USE)
        #     List of version strings (e.g., ["V3", "V2"]). Used to construct
        #     versioned data paths. Set via `_survey_kit_versions_`. Default is [].
        # latest_version : str
        #     (IGNORE - INTENDED FOR FUTURE USE)
        #     Most recent version string from versions list (read-only).
        # data_with_version : str
        #     (IGNORE - INTENDED FOR FUTURE USE)
        #     Path combining data_root with latest_version (read-only).

        _code_root_key = "_survey_kit_code_root_"
        _data_root_key = "_survey_kit_data_root_"
        _version_key = "_survey_kit_versions_"
        _cpus_key = "_survey_kit_n_cpus_"
        _path_temp_files_key = "_survey_kit_path_temp_files_"
        _ram_key = "_survey_kit_ram_"
        _parameter_files_key = "_survey_kit_parameter_files_"
        _pbs_log_path_key = "_survey_kit_pbs_log_path_"

        code_root = TypedEnvVar(_code_root_key, default="", convert=str)
        data_root = TypedEnvVar(_data_root_key, default="", convert=str)
        versions = TypedEnvVar(_version_key, default=[], convert=list)
        _cpus = TypedEnvVar(_cpus_key, os.cpu_count(), int)
        _path_temp_files = TypedEnvVar(_path_temp_files_key, "", str)
        ram = TypedEnvVar(_ram_key, psutil.virtual_memory().total)
        parameter_files = TypedEnvVar(_parameter_files_key, {}, convert=dict)
        pbs_log_path = TypedEnvVar(_pbs_log_path_key, "", str)

        @property
        def latest_version(self) -> str:
            versions = self.versions

            if len(versions):
                return versions[0]

            return ""

        @property
        def data_with_version(self) -> str:
            versions = self.versions

            output = self.data_root
            if len(versions):
                latest = str(versions[0])
                output = os.path.join(output, latest)

            return output

        @property
        def cpus(self) -> int:
            return self._cpus

        @cpus.setter
        def cpus(self, value: int):
            self._cpus = value

            self._set_thread_limits()

        @property
        def path_temp_files(self) -> int:
            if self._path_temp_files != "":
                return self._path_temp_files
            else:
                if self.data_root == "":
                    from .. import logger

                    message = "You must set Configs().data_root to get a default temp file directory"
                    logger.error(message)
                    raise Exception(message)

                return Path(self.data_root) / "temp_files"

        @path_temp_files.setter
        def path_temp_files(self, value: str):
            self._path_temp_files = value

        def _set_thread_limits(self):
            n_cpus = self.cpus

            cpu_limits = [
                "POLARS_MAX_THREADS",
                "OMP_NUM_THREADS",
                "NUMEXPR_NUM_THREADS",
                "MKL_NUM_THREADS",
                "OPENBLAS_NUM_THREADS",
            ]
            for limiti in cpu_limits:
                os.environ[limiti] = str(n_cpus)

        @property
        def mem_in_gb(self) -> int:
            return self._mem("gb")

        @property
        def mem_in_mb(self) -> int:
            return self._mem("mb")

        @property
        def mem_in_kb(self) -> int:
            return self._mem("kb")

        def _mem(self, unit: str) -> int:
            unit = unit.lower()

            if unit == "gb":
                power = 3
            elif unit == "mb":
                power = 2
            elif unit == "kb":
                power = 1
            else:
                from .. import logger

                message = f"Must pass kb, mb, or gb ({unit})"
                from .. import logger

                logger.error(message)
                raise Exception(message)

            return int(self.ram / 1024**power)

        def path_temp_with_random(
            self, as_parquet: bool = False, underscore_prefix: bool = False
        ) -> str:
            """
            Generate a random temporary file path.

            Parameters
            ----------
            as_parquet : bool, optional
                Add .parquet extension. Default is False.
            underscore_prefix : bool, optional
                Prefix filename with underscore. Default is False.

            Returns
            -------
            str
                Full path to a uniquely-named temporary file.

            Examples
            --------
            >>> config.path_temp_with_random()
            '/data/temp_files/abc123xyz'

            >>> config.path_temp_with_random(as_parquet=True)
            '/data/temp_files/abc123xyz.parquet'
            """
            if as_parquet:
                parquet_suffix = ".parquet"
            else:
                parquet_suffix = ""

            if underscore_prefix:
                prefix = "_"
            else:
                prefix = ""

            return os.path.normpath(
                f"{self.path_temp_files}/{prefix}{next(tempfile._get_candidate_names())}{parquet_suffix}"
            )

        def clean_temp_directory(self, clean_older_than_days: int = 7):
            from .. import logger

            try:
                path_to_clean = self.path_temp_files

                CleanTempDirectory.clean_old_files(
                    temp_dir_path=path_to_clean, clean_older_than_days=clean_older_than_days
                )
            except:
                from .. import logger

                logger.info("Not temp directory to clean")

### path_temp_with_random

    path_temp_with_random(
        as_parquet: bool = False,
        underscore_prefix: bool = False,
    ) -> str

Generate a random temporary file path.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`as_parquet` | `bool` | Add .parquet extension. Default is False. | `False`
`underscore_prefix` | `bool` | Prefix filename with underscore. Default is False. | `False`

Returns:

Type | Description
--- | ---
`str` | Full path to a uniquely-named temporary file.

Examples:

    >>> config.path_temp_with_random()
    '/data/temp_files/abc123xyz'

    >>> config.path_temp_with_random(as_parquet=True)
    '/data/temp_files/abc123xyz.parquet'

Source code in `src\survey_kit\orchestration\config.py`

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    def path_temp_with_random(
        self, as_parquet: bool = False, underscore_prefix: bool = False
    ) -> str:
        """
        Generate a random temporary file path.

        Parameters
        ----------
        as_parquet : bool, optional
            Add .parquet extension. Default is False.
        underscore_prefix : bool, optional
            Prefix filename with underscore. Default is False.

        Returns
        -------
        str
            Full path to a uniquely-named temporary file.

        Examples
        --------
        >>> config.path_temp_with_random()
        '/data/temp_files/abc123xyz'

        >>> config.path_temp_with_random(as_parquet=True)
        '/data/temp_files/abc123xyz.parquet'
        """
        if as_parquet:
            parquet_suffix = ".parquet"
        else:
            parquet_suffix = ""

        if underscore_prefix:
            prefix = "_"
        else:
            prefix = ""

        return os.path.normpath(
            f"{self.path_temp_files}/{prefix}{next(tempfile._get_candidate_names())}{parquet_suffix}"
        )

## random

### RandomData

Generate random data, in a slightly easier way.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`n_rows` | `int` | Number of rows in the data to be generated | required
`seed` | `int` | For replicability, set the seed. Default is 0 (which does not set the seed) | `0`

Examples:

Create a dataframe with random variables:

    >>> nRows = 10000
    >>> df = (RandomData(n_rows=nRows, seed=89465551)
    ...       .index("index")
    ...       .integer("year", lower=2015, upper=2020)
    ...       .float("var1", 0, 100000)
    ...       .integer("var2", 1, 100000)
    ...       .integer("var3", 1, 50)
    ...       .float("var4", 0, 1)
    ...       .date("date", date(2020, 1, 1), date(2025, 12, 31))
    ...       .datetime("datetime", date(2020, 1, 1), date(2025, 12, 31))
    ...       .np_distribution("v_normal", "normal", dict(loc=1, scale=2))
    ...       .np_distribution("v_lognormal", "lognormal", dict(mean=1, sigma=2))
    ...       .to_df()
    ... )

Source code in `src\survey_kit\utilities\random.py`

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    class RandomData:
        """
        Generate random data, in a slightly easier way.

        Parameters
        ----------
        n_rows : int
            Number of rows in the data to be generated
        seed : int, optional
            For replicability, set the seed.
            Default is 0 (which does not set the seed)

        Examples
        --------
        Create a dataframe with random variables:

        >>> nRows = 10000
        >>> df = (RandomData(n_rows=nRows, seed=89465551)
        ...       .index("index")
        ...       .integer("year", lower=2015, upper=2020)
        ...       .float("var1", 0, 100000)
        ...       .integer("var2", 1, 100000)
        ...       .integer("var3", 1, 50)
        ...       .float("var4", 0, 1)
        ...       .date("date", date(2020, 1, 1), date(2025, 12, 31))
        ...       .datetime("datetime", date(2020, 1, 1), date(2025, 12, 31))
        ...       .np_distribution("v_normal", "normal", dict(loc=1, scale=2))
        ...       .np_distribution("v_lognormal", "lognormal", dict(mean=1, sigma=2))
        ...       .to_df()
        ... )
        """

        def __init__(self, n_rows: int, seed: int = 0):
            if seed > 0:
                set_seed(seed)

            self.rng = RandomNumberGenerator()
            self.n_rows = n_rows
            self._data = {}

        def index(self, name: str) -> RandomData:
            """
            Create an index column (i.e. 0-n_rows-1)

            Parameters
            ----------
            name : str
                column name

            Returns
            -------
            RandomData object (so you can chain 's)
            """
            self._data[name] = range(0, self.n_rows)

            return self

        def boolean(self, name: str) -> RandomData:
            """
            Create an boolean column

            Parameters
            ----------
            name : str
                column name

            Returns
            -------
            RandomData object (so you can chain 's)
            """

            self._data[name] = np.ceil(self.rng.uniform(low=-1, high=1, size=self.n_rows))

            return self

        def integer(self, name: str, lower: int, upper: int) -> RandomData:
            """
            Create an integer column in [lower,upper]

            Parameters
            ----------
            name : str
                column name
            lower : int
                lower bound (inclusive)
            upper : int
                upper bound (inclusive)

            Returns
            -------
            RandomData object (so you can chain 's)
            """

            self._data[name] = np.ceil(
                self.rng.uniform(low=lower - 1, high=upper, size=self.n_rows)
            )

            return self

        def float(self, name: str, lower: float, upper: float) -> RandomData:
            """
            Create a float64 column in (lower,upper)

            Parameters
            ----------
            name : str
                column name
            lower : float
                lower bound
            upper : float
                upper bound

            Returns
            -------
            RandomData object (so you can chain 's)
            """

            self._data[name] = self.rng.uniform(low=lower, high=upper, size=self.n_rows)

            return self

        def date(self, name: str, start: date, end: date) -> RandomData:
            """
            Create a date column in [start,end]

            Parameters
            ----------
            name : str
                column name
            start : date
                lower bound
            end : date
                upper bound

            Returns
            -------
            RandomData object (so you can chain 's)
            """
            self._data[name] = pl.date_range(start, end, "1d", eager=True).sample(
                n=self.n_rows, with_replacement=True
            )

            return self

        def datetime(
            self, name: str, start: datetime | date, end: datetime | date
        ) -> RandomData:
            """
            Create a datetime column in [start,end]

            Parameters
            ----------
            name : str
                column name
            start : datetime | date
                lower bound
            end : datetime | date
                upper bound

            Returns
            -------
            RandomData object (so you can chain 's)
            """

            self._data[name] = pl.datetime_range(start, end, "1m", eager=True).sample(
                n=self.n_rows, with_replacement=True
            )

            return self

        def np_distribution(self, name: str, distribution: str, **kwargs) -> RandomData:
            """
            Create a column from a numpy distribution.

            Parameters
            ----------
            name : str
                Column name.
            distribution : str
                Name of numpy random distribution (e.g., "normal", "lognormal", "exponential").
                Must be a valid method of np.random.Generator.
            **kwargs
                Keyword arguments passed to the distribution function (e.g., loc, scale for normal).

            Returns
            -------
            RandomData
                Self for method chaining.

            Raises
            ------
            Exception
                If distribution is not a valid numpy random distribution.

            Examples
            --------
            >>> df = (RandomData(n_rows=1000, seed=123)
            ...       .np_distribution("normal_var", "normal", loc=0, scale=1)
            ...       .np_distribution("lognormal_var", "lognormal", mean=1, sigma=2)
            ...       .np_distribution("exponential_var", "exponential", scale=1.5)
            ...       .to_df())
            """

            if hasattr(self.rng, distribution):
                generator = getattr(self.rng, distribution)
                self._data[name] = generator(size=self.n_rows, **kwargs)
            else:
                message = f"Numpy generator does not have the function '{distribution}'"
                logger.error(message)
                raise Exception(message)

            return self

        def to_df(self, compress: bool = True) -> pl.DataFrame:
            """
            Convert accumulated random data to a Polars DataFrame.

            Parameters
            ----------
            compress : bool, optional
                Apply compression to reduce memory usage by downcasting numeric types.
                Default is True.

            Returns
            -------
            pl.DataFrame
                DataFrame containing all generated columns.

            Examples
            --------
            >>> df = (RandomData(n_rows=1000, seed=123)
            ...       .integer("id", 1, 1000)
            ...       .float("value", 0, 100)
            ...       .to_df())
            >>> print(df)

            >>> # Without compression
            >>> df_uncompressed = (RandomData(n_rows=1000, seed=123)
            ...                    .integer("id", 1, 1000)
            ...                    .to_df(compress=False))
            """

            df = pl.DataFrame(self._data)
            if compress:
                return compress_df(df)
            else:
                return df

#### boolean

    boolean(name: str) -> RandomData

Create an boolean column

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`name` | `str` | column name | required

Returns:

Type | Description
--- | ---
`RandomData object (so you can chain 's)` |

Source code in `src\survey_kit\utilities\random.py`

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    def boolean(self, name: str) -> RandomData:
        """
        Create an boolean column

        Parameters
        ----------
        name : str
            column name

        Returns
        -------
        RandomData object (so you can chain 's)
        """

        self._data[name] = np.ceil(self.rng.uniform(low=-1, high=1, size=self.n_rows))

        return self

#### date

    date(name: str, start: date, end: date) -> RandomData

Create a date column in [start,end]

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`name` | `str` | column name | required
`start` | `date` | lower bound | required
`end` | `date` | upper bound | required

Returns:

Type | Description
--- | ---
`RandomData object (so you can chain 's)` |

Source code in `src\survey_kit\utilities\random.py`

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    def date(self, name: str, start: date, end: date) -> RandomData:
        """
        Create a date column in [start,end]

        Parameters
        ----------
        name : str
            column name
        start : date
            lower bound
        end : date
            upper bound

        Returns
        -------
        RandomData object (so you can chain 's)
        """
        self._data[name] = pl.date_range(start, end, "1d", eager=True).sample(
            n=self.n_rows, with_replacement=True
        )

        return self

#### datetime

    datetime(
        name: str, start: datetime | date, end: datetime | date
    ) -> RandomData

Create a datetime column in [start,end]

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`name` | `str` | column name | required
`start` | `datetime | date` | lower bound | required
`end` | `datetime | date` | upper bound | required

Returns:

Type | Description
--- | ---
`RandomData object (so you can chain 's)` |

Source code in `src\survey_kit\utilities\random.py`

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    def datetime(
        self, name: str, start: datetime | date, end: datetime | date
    ) -> RandomData:
        """
        Create a datetime column in [start,end]

        Parameters
        ----------
        name : str
            column name
        start : datetime | date
            lower bound
        end : datetime | date
            upper bound

        Returns
        -------
        RandomData object (so you can chain 's)
        """

        self._data[name] = pl.datetime_range(start, end, "1m", eager=True).sample(
            n=self.n_rows, with_replacement=True
        )

        return self

#### float

    float(name: str, lower: float, upper: float) -> RandomData

Create a float64 column in (lower,upper)

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`name` | `str` | column name | required
`lower` | `float` | lower bound | required
`upper` | `float` | upper bound | required

Returns:

Type | Description
--- | ---
`RandomData object (so you can chain 's)` |

Source code in `src\survey_kit\utilities\random.py`

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    def float(self, name: str, lower: float, upper: float) -> RandomData:
        """
        Create a float64 column in (lower,upper)

        Parameters
        ----------
        name : str
            column name
        lower : float
            lower bound
        upper : float
            upper bound

        Returns
        -------
        RandomData object (so you can chain 's)
        """

        self._data[name] = self.rng.uniform(low=lower, high=upper, size=self.n_rows)

        return self

#### index

    index(name: str) -> RandomData

Create an index column (i.e. 0-n_rows-1)

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`name` | `str` | column name | required

Returns:

Type | Description
--- | ---
`RandomData object (so you can chain 's)` |

Source code in `src\survey_kit\utilities\random.py`

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    def index(self, name: str) -> RandomData:
        """
        Create an index column (i.e. 0-n_rows-1)

        Parameters
        ----------
        name : str
            column name

        Returns
        -------
        RandomData object (so you can chain 's)
        """
        self._data[name] = range(0, self.n_rows)

        return self

#### integer

    integer(name: str, lower: int, upper: int) -> RandomData

Create an integer column in [lower,upper]

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`name` | `str` | column name | required
`lower` | `int` | lower bound (inclusive) | required
`upper` | `int` | upper bound (inclusive) | required

Returns:

Type | Description
--- | ---
`RandomData object (so you can chain 's)` |

Source code in `src\survey_kit\utilities\random.py`

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    def integer(self, name: str, lower: int, upper: int) -> RandomData:
        """
        Create an integer column in [lower,upper]

        Parameters
        ----------
        name : str
            column name
        lower : int
            lower bound (inclusive)
        upper : int
            upper bound (inclusive)

        Returns
        -------
        RandomData object (so you can chain 's)
        """

        self._data[name] = np.ceil(
            self.rng.uniform(low=lower - 1, high=upper, size=self.n_rows)
        )

        return self

#### np_distribution

    np_distribution(
        name: str, distribution: str, **kwargs
    ) -> RandomData

Create a column from a numpy distribution.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`name` | `str` | Column name. | required
`distribution` | `str` | Name of numpy random distribution (e.g., "normal", "lognormal", "exponential"). Must be a valid method of np.random.Generator. | required
`**kwargs` |  | Keyword arguments passed to the distribution function (e.g., loc, scale for normal). | `{}`

Returns:

Type | Description
--- | ---
`RandomData` | Self for method chaining.

Raises:

Type | Description
--- | ---
`Exception` | If distribution is not a valid numpy random distribution.

Examples:

    >>> df = (RandomData(n_rows=1000, seed=123)
    ...       .np_distribution("normal_var", "normal", loc=0, scale=1)
    ...       .np_distribution("lognormal_var", "lognormal", mean=1, sigma=2)
    ...       .np_distribution("exponential_var", "exponential", scale=1.5)
    ...       .to_df())

Source code in `src\survey_kit\utilities\random.py`

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    def np_distribution(self, name: str, distribution: str, **kwargs) -> RandomData:
        """
        Create a column from a numpy distribution.

        Parameters
        ----------
        name : str
            Column name.
        distribution : str
            Name of numpy random distribution (e.g., "normal", "lognormal", "exponential").
            Must be a valid method of np.random.Generator.
        **kwargs
            Keyword arguments passed to the distribution function (e.g., loc, scale for normal).

        Returns
        -------
        RandomData
            Self for method chaining.

        Raises
        ------
        Exception
            If distribution is not a valid numpy random distribution.

        Examples
        --------
        >>> df = (RandomData(n_rows=1000, seed=123)
        ...       .np_distribution("normal_var", "normal", loc=0, scale=1)
        ...       .np_distribution("lognormal_var", "lognormal", mean=1, sigma=2)
        ...       .np_distribution("exponential_var", "exponential", scale=1.5)
        ...       .to_df())
        """

        if hasattr(self.rng, distribution):
            generator = getattr(self.rng, distribution)
            self._data[name] = generator(size=self.n_rows, **kwargs)
        else:
            message = f"Numpy generator does not have the function '{distribution}'"
            logger.error(message)
            raise Exception(message)

        return self

#### to_df

    to_df(compress: bool = True) -> pl.DataFrame

Convert accumulated random data to a Polars DataFrame.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`compress` | `bool` | Apply compression to reduce memory usage by downcasting numeric types. Default is True. | `True`

Returns:

Type | Description
--- | ---
`DataFrame` | DataFrame containing all generated columns.

Examples:

    >>> df = (RandomData(n_rows=1000, seed=123)
    ...       .integer("id", 1, 1000)
    ...       .float("value", 0, 100)
    ...       .to_df())
    >>> print(df)

    >>> # Without compression
    >>> df_uncompressed = (RandomData(n_rows=1000, seed=123)
    ...                    .integer("id", 1, 1000)
    ...                    .to_df(compress=False))

Source code in `src\survey_kit\utilities\random.py`

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    def to_df(self, compress: bool = True) -> pl.DataFrame:
        """
        Convert accumulated random data to a Polars DataFrame.

        Parameters
        ----------
        compress : bool, optional
            Apply compression to reduce memory usage by downcasting numeric types.
            Default is True.

        Returns
        -------
        pl.DataFrame
            DataFrame containing all generated columns.

        Examples
        --------
        >>> df = (RandomData(n_rows=1000, seed=123)
        ...       .integer("id", 1, 1000)
        ...       .float("value", 0, 100)
        ...       .to_df())
        >>> print(df)

        >>> # Without compression
        >>> df_uncompressed = (RandomData(n_rows=1000, seed=123)
        ...                    .integer("id", 1, 1000)
        ...                    .to_df(compress=False))
        """

        df = pl.DataFrame(self._data)
        if compress:
            return compress_df(df)
        else:
            return df

### RandomNumberGenerator

    RandomNumberGenerator() -> np.random.Generator

Create a new numpy random number generator with random seed.

Returns:

Type | Description
--- | ---
`Generator` | Numpy random number generator initialized with a random seed from Python's random module.

Examples:

    >>> from survey_kit.utilities.random import RandomNumberGenerator
    >>> rng = RandomNumberGenerator()
    >>> random_values = rng.normal(loc=0, scale=1, size=100)

Source code in `src\survey_kit\utilities\random.py`

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    def RandomNumberGenerator() -> np.random.Generator:
        """
        Create a new numpy random number generator with random seed.

        Returns
        -------
        np.random.Generator
            Numpy random number generator initialized with a random seed
            from Python's random module.

        Examples
        --------
        >>> from survey_kit.utilities.random import RandomNumberGenerator
        >>> rng = RandomNumberGenerator()
        >>> random_values = rng.normal(loc=0, scale=1, size=100)
        """
        return np.random.default_rng(random.randint(1, 2**63 - 1))

### generate_seed

    generate_seed(power_of_2_limit: int = 32)

Generate a random seed value.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`power_of_2_limit` | `int` | Upper bound is 2^power_of_2_limit. Default is 32. | `32`

Returns:

Type | Description
--- | ---
`int` | Random integer between 1 and 2^power_of_2_limit - 1.

Examples:

    >>> from survey_kit.utilities.random import generate_seed, set_seed
    >>> seed = generate_seed()
    >>> set_seed(seed)  # Use for reproducibility

Source code in `src\survey_kit\utilities\random.py`

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    def generate_seed(power_of_2_limit: int = 32):
        """
        Generate a random seed value.

        Parameters
        ----------
        power_of_2_limit : int, optional
            Upper bound is 2^power_of_2_limit. Default is 32.

        Returns
        -------
        int
            Random integer between 1 and 2^power_of_2_limit - 1.

        Examples
        --------
        >>> from survey_kit.utilities.random import generate_seed, set_seed
        >>> seed = generate_seed()
        >>> set_seed(seed)  # Use for reproducibility
        """
        rng = RandomNumberGenerator()
        return int(rng.integers(1, 2**power_of_2_limit - 1, 1)[0])

### get_random_state

    get_random_state()

Get the current state of the random number generator.

Returns:

Type | Description
--- | ---
`tuple` | Internal state of Python's random module.

Examples:

    >>> from survey_kit.utilities.random import get_random_state, set_random_state
    >>> state = get_random_state()
    >>> # ... do some random operations ...
    >>> set_random_state(state)  # Restore to previous state

Source code in `src\survey_kit\utilities\random.py`

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    def get_random_state():
        """
        Get the current state of the random number generator.

        Returns
        -------
        tuple
            Internal state of Python's random module.

        Examples
        --------
        >>> from survey_kit.utilities.random import get_random_state, set_random_state
        >>> state = get_random_state()
        >>> # ... do some random operations ...
        >>> set_random_state(state)  # Restore to previous state
        """
        return random.getstate()

### set_random_state

    set_random_state(value)

Set the random number generator to a specific state.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`value` | `tuple` | State tuple from get_random_state(). | required

Examples:

    >>> state = get_random_state()
    >>> # ... do some random operations ...
    >>> set_random_state(state)  # Restore to previous state

Source code in `src\survey_kit\utilities\random.py`

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    def set_random_state(value):
        """
        Set the random number generator to a specific state.

        Parameters
        ----------
        value : tuple
            State tuple from get_random_state().

        Examples
        --------
        >>> state = get_random_state()
        >>> # ... do some random operations ...
        >>> set_random_state(state)  # Restore to previous state
        """
        random.setstate(value)

### set_seed

    set_seed(seed: int = 0)

Set the random seed for reproducibility.

Sets the seed for Python's random module, which is used by the random number generator in this module.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`seed` | `int` | Random seed. If 0 or less, does not set seed. Default is 0. | `0`

Examples:

    >>> from survey_kit.utilities.random import set_seed, generate_seed
    >>> set_seed(12345)
    >>> seed1 = generate_seed()
    >>> set_seed(12345)
    >>> seed2 = generate_seed()
    >>> assert seed1 == seed2  # Same seed produces same sequence

Source code in `src\survey_kit\utilities\random.py`

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    def set_seed(seed: int = 0):
        """
        Set the random seed for reproducibility.

        Sets the seed for Python's random module, which is used by the
        random number generator in this module.

        Parameters
        ----------
        seed : int, optional
            Random seed. If 0 or less, does not set seed. Default is 0.

        Examples
        --------
        >>> from survey_kit.utilities.random import set_seed, generate_seed
        >>> set_seed(12345)
        >>> seed1 = generate_seed()
        >>> set_seed(12345)
        >>> seed2 = generate_seed()
        >>> assert seed1 == seed2  # Same seed produces same sequence
        """

        if seed > 0:
            random.seed(seed)

## FormulaBuilder

Build and manipulate R-style formulas for statistical models.

FormulaBuilder provides a programmatic way to construct complex model formulas using R/Patsy-style syntax. It supports formula manipulation, variable expansion, interactions, transformations, and pattern matching against dataframes.

The class works with Formulaic to parse and expand formulas, and integrates with the dataframe utilities to resolve wildcards and column patterns.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`df` | `IntoFrameT | None` | Reference dataframe for resolving column names and wildcards. Default is None. | `None`
`formula` | `str` | Initial formula string. If empty, constructs from lhs and constant. Default is "". | `''`
`lhs` | `str` | Left-hand side of formula (response variable). Default is "". | `''`
`constant` | `bool` | Include intercept term (1) or suppress it (0). Default is True. | `True`

Attributes:

Name | Type | Description
--- | --- | ---
`formula` | `str` | Current formula string.
`df` | `IntoFrameT | None` | Reference dataframe.
`columns` | `list[str]` | All variables required by the formula.
`columns_rhs` | `list[str]` | Variables in right-hand side of formula.
`columns_lhs` | `list[str]` | Variables in left-hand side of formula.

Examples:

Basic formula construction:

    >>> from survey_kit.utilities.formula import FormulaBuilder
    >>>
    >>> fb = FormulaBuilder(df=df, lhs="income", constant=True)
    >>> fb += "age + education"
    >>> print(fb.formula)
    'income~1+age+education'

Add variables using wildcards:

    >>> fb = FormulaBuilder(df=df, lhs="income")
    >>> fb.continuous(columns="demographic_*")
    >>> fb.factor(columns="region")
    >>> print(fb.formula)

Polynomial terms:

    >>> fb = FormulaBuilder(df=df, lhs="income")
    >>> fb.polynomial(columns="age", degree=3)
    >>> print(fb.formula)
    'income~1+poly(age,degree=3,raw=True)'

Interactions:

    >>> fb = FormulaBuilder(df=df, lhs="income")
    >>> fb.simple_interaction(columns=["age", "education"], order=2)
    >>> print(fb.formula)
    'income~1+(age+education)**2'

Standardization:

    >>> fb = FormulaBuilder(df=df, lhs="income")
    >>> fb.scale(columns=["age", "experience"])
    >>> print(fb.formula)
    'income~1+scale(age)+scale(experience)'

Working with existing formula strings:

    >>> formula = "income~age+education+age:education"
    >>> fb = FormulaBuilder(df=df, formula=formula)
    >>> fb.expand()  # Expand shorthand
    >>> print(fb.rhs())  # Get right-hand side
    'age+education+age:education'
    >>> print(fb.columns)
    ['income', 'age', 'education']

Notes

Formula syntax follows R/Patsy conventions: - `~` separates left and right sides - `+` adds terms - `:` creates interactions - `*` creates main effects and interactions: `a*b` = `a+b+a:b` - `**n` creates all n-way interactions - `I()` for arithmetic operations - `C()` for categorical variables - Functions like `scale()`, `center()`, `poly()` for transformations

Wildcards in column specifications: - `"income_*"` matches all columns starting with "income_" - `["age", "education_*"]` matches age and all education columns

The FormulaBuilder can be used in two modes: 1. Object mode: Create instance and chain methods 2. Static mode: Call methods with self=None for one-off operations

See Also

formulaic.Formula : Underlying formula parser

Source code in `src\survey_kit\utilities\formula_builder.py`

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    class FormulaBuilder:
        """
        Build and manipulate R-style formulas for statistical models.

        FormulaBuilder provides a programmatic way to construct complex model formulas
        using R/Patsy-style syntax. It supports formula manipulation, variable expansion,
        interactions, transformations, and pattern matching against dataframes.

        The class works with Formulaic to parse and expand formulas, and integrates
        with the dataframe utilities to resolve wildcards and column patterns.

        Parameters
        ----------
        df : IntoFrameT | None, optional
            Reference dataframe for resolving column names and wildcards.
            Default is None.
        formula : str, optional
            Initial formula string. If empty, constructs from lhs and constant.
            Default is "".
        lhs : str, optional
            Left-hand side of formula (response variable). Default is "".
        constant : bool, optional
            Include intercept term (1) or suppress it (0). Default is True.

        Attributes
        ----------
        formula : str
            Current formula string.
        df : IntoFrameT | None
            Reference dataframe.
        columns : list[str]
            All variables required by the formula.
        columns_rhs : list[str]
            Variables in right-hand side of formula.
        columns_lhs : list[str]
            Variables in left-hand side of formula.

        Examples
        --------
        Basic formula construction:

        >>> from survey_kit.utilities.formula import FormulaBuilder
        >>>
        >>> fb = FormulaBuilder(df=df, lhs="income", constant=True)
        >>> fb += "age + education"
        >>> print(fb.formula)
        'income~1+age+education'

        Add variables using wildcards:

        >>> fb = FormulaBuilder(df=df, lhs="income")
        >>> fb.continuous(columns="demographic_*")
        >>> fb.factor(columns="region")
        >>> print(fb.formula)

        Polynomial terms:

        >>> fb = FormulaBuilder(df=df, lhs="income")
        >>> fb.polynomial(columns="age", degree=3)
        >>> print(fb.formula)
        'income~1+poly(age,degree=3,raw=True)'

        Interactions:

        >>> fb = FormulaBuilder(df=df, lhs="income")
        >>> fb.simple_interaction(columns=["age", "education"], order=2)
        >>> print(fb.formula)
        'income~1+(age+education)**2'

        Standardization:

        >>> fb = FormulaBuilder(df=df, lhs="income")
        >>> fb.scale(columns=["age", "experience"])
        >>> print(fb.formula)
        'income~1+scale(age)+scale(experience)'

        Working with existing formula strings:

        >>> formula = "income~age+education+age:education"
        >>> fb = FormulaBuilder(df=df, formula=formula)
        >>> fb.expand()  # Expand shorthand
        >>> print(fb.rhs())  # Get right-hand side
        'age+education+age:education'
        >>> print(fb.columns)
        ['income', 'age', 'education']

        Notes
        -----
        Formula syntax follows R/Patsy conventions:
        - `~` separates left and right sides
        - `+` adds terms
        - `:` creates interactions
        - `*` creates main effects and interactions: `a*b` = `a+b+a:b`
        - `**n` creates all n-way interactions
        - `I()` for arithmetic operations
        - `C()` for categorical variables
        - Functions like `scale()`, `center()`, `poly()` for transformations

        Wildcards in column specifications:
        - `"income_*"` matches all columns starting with "income_"
        - `["age", "education_*"]` matches age and all education columns

        The FormulaBuilder can be used in two modes:
        1. Object mode: Create instance and chain methods
        2. Static mode: Call methods with self=None for one-off operations

        See Also
        --------
        formulaic.Formula : Underlying formula parser
        """

        def __init__(
            self,
            df: IntoFrameT | None = None,
            formula: str = "",
            lhs: str = "",
            constant: bool = True,
        ):
            if formula == "":
                self.formula = f"{lhs}~{int(constant)}"
            else:
                self.formula = formula

            if df is not None:
                self.df = nw.from_native(df).head(0).to_native()
            else:
                self.df = None

        def __str__(self):
            return self.formula

        def __add__(self, o):
            """Add terms to the formula using + operator."""
            if type(o) is FormulaBuilder:
                o = o.rhs()

            o = str(o)
            if o.startswith("~"):
                o = o[1 : len(o)]

            if o.startswith("1+"):
                o = o[2 : len(o)]
            if o.startswith("0+"):
                o = o[2 : len(o)]

            self.formula = f"{self.formula}+{o}"

            self.expand()

            return self

        def add_to_formula(self, add_part: str = "", plus_first: bool = True) -> None:
            """
            Append a string to the formula.

            Parameters
            ----------
            add_part : str, optional
                String to append. Default is "".
            plus_first : bool, optional
                Add "+" before the string. Default is True.
            """
            if plus_first:
                plus = "+"
            else:
                plus = ""
            self.formula += f"{plus}{add_part}"

        def any_wrapper(
            self=None,
            df: IntoFrameT | None = None,
            columns: str | list | None = None,
            clause: str = "",
            case_insensitive: bool = False,
            prefix: str = "",
            suffix: str = "",
        ) -> str | FormulaBuilder:
            """
            General wrapper for adding columns with prefix/suffix.

            Used internally by other methods to add variables with transformations.

            Parameters
            ----------
            df : IntoFrameT | None, optional
                Dataframe for resolving column patterns. Default is None.
            columns : str | list | None, optional
                Column names or patterns. Default is None.
            clause : str, optional
                Pre-constructed clause (bypasses column lookup). Default is "".
            case_insensitive : bool, optional
                Case-insensitive column matching. Default is False.
            prefix : str, optional
                String to prepend to each column. Default is "".
            suffix : str, optional
                String to append to each column. Default is "".

            Returns
            -------
            str | FormulaBuilder
                Formula string if self is None, otherwise returns self for chaining.
            """
            columns = list_input(columns)

            #   Dataframe to look for columns in
            if df is None:
                df = self.df

            if clause != "":
                output = f"+{prefix}{clause}{suffix}"
            else:
                if df is not None:
                    columns = columns_from_list(
                        df=df, columns=columns, case_insensitive=case_insensitive
                    )

                out_list = [f"{prefix}{coli}{suffix}" for coli in columns]
                output = "+" + "+".join(out_list)

            if self is None:
                return output
            else:
                self.formula += output
                return self

        def continuous(
            self=None,
            df: IntoFrameT | None = None,
            columns: str | list | None = None,
            clause: str = "",
            case_insensitive: bool = False,
        ) -> str | FormulaBuilder:
            """
            Add continuous variables to the formula.

            Parameters
            ----------
            df : IntoFrameT | None, optional
                Dataframe for column lookup. Default is None.
            columns : str | list | None, optional
                Column names or patterns (e.g., "income_*"). Default is None.
            clause : str, optional
                Pre-constructed clause. Default is "".
            case_insensitive : bool, optional
                Case-insensitive matching. Default is False.

            Returns
            -------
            str | FormulaBuilder
                Formula string or self for chaining.

            Examples
            --------
            >>> fb = FormulaBuilder(df=df, lhs="y")
            >>> fb.continuous(columns=["age", "income"])
            >>> print(fb.formula)
            'y~1+age+income'
            """
            if self is None:
                caller = FormulaBuilder
            else:
                caller = self

            return caller.any_wrapper(
                df=df, columns=columns, clause=clause, case_insensitive=case_insensitive
            )

        def function(
            self=None,
            df: IntoFrameT | None = None,
            columns: str | list | None = None,
            clause: str = "",
            operator_before: str = "",
            operator_after: str = "",
            function_item: str = "",
            case_insensitive: bool = False,
            **kwargs,
        ) -> str | FormulaBuilder:
            """
            Wrap columns in a function call.

            Parameters
            ----------
            df : IntoFrameT | None, optional
                Dataframe for column lookup. Default is None.
            columns : str | list | None, optional
                Column names or patterns. Default is None.
            clause : str, optional
                Pre-constructed clause. Default is "".
            operator_before : str, optional
                String before function call. Default is "".
            operator_after : str, optional
                String after function call. Default is "".
            function_item : str, optional
                Function name (e.g., "log", "sqrt"). Default is "".
            case_insensitive : bool, optional
                Case-insensitive matching. Default is False.
            **kwargs
                Additional function arguments.

            Returns
            -------
            str | FormulaBuilder
                Formula string or self for chaining.

            Examples
            --------
            >>> fb.function(columns="income", function_item="log")
            >>> print(fb.formula)
            'y~1+log(income)'
            """
            if self is None:
                caller = FormulaBuilder
            else:
                caller = self

            if function_item != "":
                operator_before = f"{operator_before}{function_item}("

                operator_after_final = ""
                if len(kwargs):
                    for keyi, valuei in kwargs.items():
                        operator_after_final += ","
                        if type(valuei) is str:
                            operator_after_final += f"{keyi}='{valuei}'"
                        else:
                            operator_after_final += f"{keyi}={valuei}"

                operator_after_final += f"{operator_after})"
            else:
                operator_after_final = operator_after

            return caller.any_wrapper(
                df=df,
                columns=columns,
                clause=clause,
                case_insensitive=case_insensitive,
                prefix=f"{{{operator_before}",
                suffix=f"{operator_after_final}}}",
            )

        def scale(
            self=None,
            df: IntoFrameT | None = None,
            columns: str | list | None = None,
            clause: str = "",
            standardize: bool = True,
            case_insensitive: bool = False,
        ) -> str | FormulaBuilder:
            """
            Standardize or center variables.

            Parameters
            ----------
            df : IntoFrameT | None, optional
                Dataframe for column lookup. Default is None.
            columns : str | list | None, optional
                Column names or patterns. Default is None.
            clause : str, optional
                Pre-constructed clause. Default is "".
            standardize : bool, optional
                If True, standardize (mean=0, sd=1). If False, only center (mean=0).
                Default is True.
            case_insensitive : bool, optional
                Case-insensitive matching. Default is False.

            Returns
            -------
            str | FormulaBuilder
                Formula string or self for chaining.

            Examples
            --------
            >>> fb.scale(columns="age")  # Standardize
            >>> fb.scale(columns="income", standardize=False)  # Center only
            """
            if self is None:
                caller = FormulaBuilder
            else:
                caller = self

            if standardize:
                function_item = "scale"
            else:
                function_item = "center"

            return caller.any_wrapper(
                df=df,
                columns=columns,
                clause=clause,
                case_insensitive=case_insensitive,
                prefix=f"{function_item}(",
                suffix=")",
            )

        def center(
            self=None,
            df: IntoFrameT | None = None,
            columns: str | list | None = None,
            clause: str = "",
            case_insensitive: bool = False,
        ) -> str | FormulaBuilder:
            """
            Center variables (subtract mean).

            Convenience method for scale(standardize=False).

            Parameters
            ----------
            df : IntoFrameT | None, optional
                Dataframe for column lookup. Default is None.
            columns : str | list | None, optional
                Column names or patterns. Default is None.
            clause : str, optional
                Pre-constructed clause. Default is "".
            case_insensitive : bool, optional
                Case-insensitive matching. Default is False.

            Returns
            -------
            str | FormulaBuilder
                Formula string or self for chaining.
            """
            if self is None:
                caller = FormulaBuilder
            else:
                caller = self

            return caller.scale(
                df=df,
                columns=columns,
                clause=clause,
                case_insensitive=case_insensitive,
                standardize=False,
            )

        def standardize(
            self=None,
            df: IntoFrameT | None = None,
            columns: str | list | None = None,
            clause: str = "",
            case_insensitive: bool = False,
        ) -> str | FormulaBuilder:
            """
            Standardize variables (mean=0, sd=1).

            Convenience method for scale(standardize=True).

            Parameters
            ----------
            df : IntoFrameT | None, optional
                Dataframe for column lookup. Default is None.
            columns : str | list | None, optional
                Column names or patterns. Default is None.
            clause : str, optional
                Pre-constructed clause. Default is "".
            case_insensitive : bool, optional
                Case-insensitive matching. Default is False.

            Returns
            -------
            str | FormulaBuilder
                Formula string or self for chaining.
            """
            if self is None:
                caller = FormulaBuilder
            else:
                caller = self

            return caller.scale(
                df=df,
                columns=columns,
                clause=clause,
                case_insensitive=case_insensitive,
                standardize=True,
            )

        def polynomial(
            self=None,
            df: IntoFrameT | None = None,
            columns: str | list | None = None,
            clause: str = "",
            degree: int = 0,
            case_insensitive: bool = False,
            center: bool = False,
        ) -> str | FormulaBuilder:
            """
            Add polynomial terms.

            Parameters
            ----------
            df : IntoFrameT | None, optional
                Dataframe for column lookup. Default is None.
            columns : str | list | None, optional
                Column names or patterns. Default is None.
            clause : str, optional
                Pre-constructed clause. Default is "".
            degree : int, optional
                Polynomial degree. Default is 0 (returns continuous).
            case_insensitive : bool, optional
                Case-insensitive matching. Default is False.
            center : bool, optional
                Use orthogonal polynomials (centered). Default is False (raw polynomials).

            Returns
            -------
            str | FormulaBuilder
                Formula string or self for chaining.

            Examples
            --------
            >>> fb.polynomial(columns="age", degree=3)
            >>> print(fb.formula)
            'y~1+poly(age,degree=3,raw=True)'

            >>> fb.polynomial(columns="age", degree=2, center=True)
            >>> print(fb.formula)
            'y~1+poly(age,degree=2,raw=False)'
            """
            if self is None:
                caller = FormulaBuilder
            else:
                caller = self
                if df is None:
                    df = self.df

            if degree <= 1:
                if center:
                    return caller.center(
                        df=df,
                        columns=columns,
                        clause=clause,
                        case_insensitive=case_insensitive,
                    )
                else:
                    return caller.continuous(
                        df=df,
                        columns=columns,
                        clause=clause,
                        case_insensitive=case_insensitive,
                    )
            else:
                subformula = ""

                for power in range(1, degree + 1):
                    operator_before = "poly("
                    if center:
                        operator_after = f",degree={degree},raw=False)"
                    else:
                        operator_after = f",degree={degree},raw=True)"

                    subformula += FormulaBuilder.function(
                        df=df,
                        columns=columns,
                        clause=clause,
                        operator_before=operator_before,
                        operator_after=operator_after,
                        case_insensitive=case_insensitive,
                    )

                if self is None:
                    return subformula
                else:
                    self.add_to_formula(subformula, False)
                    return self

        def simple_interaction(
            self=None,
            df: IntoFrameT | None = None,
            columns: str | list | None = None,
            order: int = 2,
            case_insensitive: bool = False,
            sub_function: Callable | None = None,
            no_base: bool = False,
        ) -> str | FormulaBuilder:
            """
            Create interactions between variables.

            Parameters
            ----------
            df : IntoFrameT | None, optional
                Dataframe for column lookup. Default is None.
            columns : str | list | None, optional
                Column names or patterns. Default is None.
            order : int, optional
                Interaction order. 2 = pairwise, 3 = three-way, etc. Default is 2.
            case_insensitive : bool, optional
                Case-insensitive matching. Default is False.
            sub_function : Callable | None, optional
                Function to apply to columns before interacting. Default is None.
            no_base : bool, optional
                Exclude main effects (only interactions). Default is False.

            Returns
            -------
            str | FormulaBuilder
                Formula string or self for chaining.

            Examples
            --------
            >>> fb.simple_interaction(columns=["age", "education"], order=2)
            >>> print(fb.formula)
            'y~1+(age+education)**2'

            >>> fb.simple_interaction(columns=["a", "b", "c"], order=2, no_base=True)
            >>> print(fb.formula)
            'y~1+(a+b+c)**2-(a+b+c)'  # Interactions only
            """
            if self is not None:
                if df is None:
                    df = self.df

            if sub_function is None:
                sub_function = FormulaBuilder.continuous

            columns = columns_from_list(
                df=df, columns=columns, case_insensitive=case_insensitive
            )

            subformula = sub_function(
                df=df, columns=columns, case_insensitive=case_insensitive
            )
            #   Remove leading plus sign
            subformula = subformula[1 : len(subformula)]

            if len(columns) > 1:
                output = f"({subformula})**{order}"
            else:
                output = subformula

            if no_base:
                output += f"-({subformula})"

            if self is None:
                return f"+{output}"
            else:
                self.add_to_formula(output)
                return self

        def interact_clauses(
            self=None, clause1: str = "", clause2: str = "", no_base: bool = False
        ) -> str | FormulaBuilder:
            """
            Create interactions between two formula clauses.

            Parameters
            ----------
            clause1 : str, optional
                First formula clause. Default is "".
            clause2 : str, optional
                Second formula clause. Default is "".
            no_base : bool, optional
                Exclude main effects from clauses. Default is False.

            Returns
            -------
            str | FormulaBuilder
                Formula string or self for chaining.

            Examples
            --------
            >>> fb.interact_clauses("age+education", "region")
            >>> print(fb.formula)
            'y~1+(age+education)*(region)'
            """
            if clause1.startswith("+"):
                clause1 = clause1[1:]
            if clause2.startswith("+"):
                clause2 = clause2[1:]

            clause1 = clause1.replace("++", "+")
            clause2 = clause2.replace("++", "+")

            output = f"({clause1})*({clause2})"

            if no_base:
                output += f"-({clause1} + {clause2})"

            if self is None:
                return f"+{output}"
            else:
                self.add_to_formula(output)
                return self

        def factor(
            self=None,
            df: IntoFrameT | None = None,
            columns: str | list | None = None,
            clause: str = "",
            reference=None,
            case_insensitive: bool = False,
        ):
            """
            Add categorical variables with treatment coding.

            Parameters
            ----------
            df : IntoFrameT | None, optional
                Dataframe for column lookup. Default is None.
            columns : str | list | None, optional
                Column names or patterns. Default is None.
            clause : str, optional
                Pre-constructed clause. Default is "".
            reference : str | int | None, optional
                Reference level for treatment coding. Default is None (use first level).
            case_insensitive : bool, optional
                Case-insensitive matching. Default is False.

            Returns
            -------
            str | FormulaBuilder
                Formula string or self for chaining.

            Examples
            --------
            >>> fb.factor(columns="region")
            >>> print(fb.formula)
            'y~1+C(region)'

            >>> fb.factor(columns="education", reference="high_school")
            >>> print(fb.formula)
            "y~1+C(education, contr.treatment('high_school'))"
            """
            if self is None:
                caller = FormulaBuilder
            else:
                caller = self

            prefix = "C("
            if reference is not None:
                if type(reference) is str:
                    suffix = f", contr.treatment('{reference}'))"
                else:
                    suffix = f", contr.treatment({reference}))"

            else:
                suffix = ")"  #  f", contr.treatment)"

            return caller.any_wrapper(
                df=df, columns=columns, clause=clause, prefix=prefix, suffix=suffix
            )

        @property
        def columns(self):
            """Get all variables required by the formula."""
            return self.columns_from_formula()

        def columns_from_formula(self=None, formula: str = "") -> list[str]:
            """
            Extract variable names from a formula.

            Parameters
            ----------
            formula : str, optional
                Formula string. If empty, uses self.formula. Default is "".

            Returns
            -------
            list[str]
                Variable names required by the formula.
            """
            if type(self) is str:
                formula = self
                self = None

            if self is not None and formula == "":
                formula = self.formula
            return list(Formula(formula).required_variables)

        def lhs(self=None, formula: str = "") -> str:
            """
            Get left-hand side of formula.

            Parameters
            ----------
            formula : str, optional
                Formula string. If empty, uses self.formula. Default is "".

            Returns
            -------
            str
                Left-hand side (response variable).
            """
            if type(self) is str:
                formula = self
                self = None

            if self is not None:
                if formula == "":
                    formula = self.formula

            #   Separate into subclauses
            sides = formula.split("~")

            if len(sides) == 2:
                lhs_string = sides[0]
            else:
                lhs_string = ""

            return lhs_string

        def rhs(self=None, formula: str = "") -> str:
            """
            Get right-hand side of formula.

            Parameters
            ----------
            formula : str, optional
                Formula string. If empty, uses self.formula. Default is "".

            Returns
            -------
            str
                Right-hand side (predictors).
            """
            if type(self) is str:
                formula = self
                self = None

            if self is not None:
                if formula == "":
                    formula = self.formula

            #   Separate into subclauses
            sides = formula.split("~")

            if len(sides) == 2:
                rhs_string = sides[1]
            else:
                rhs_string = sides[0]

            return rhs_string

        @property
        def columns_rhs(self):
            """Variables in right-hand side, ordered as in dataframe."""
            formula_rhs = self.rhs()
            columns = FormulaBuilder.columns_from_formula(formula=formula_rhs)

            if self.df is not None:
                return _columns_original_order(
                    columns_unordered=columns,
                    columns_ordered=nw.from_native(self.df).lazy().collect_schema().names(),
                )
            else:
                return columns

        @property
        def columns_lhs(self):
            """Variables in left-hand side, ordered as in dataframe."""
            formula_lhs = self.lhs()
            if formula_lhs == "":
                return []
            else:
                columns = FormulaBuilder.columns_from_formula(formula=formula_lhs)
                if len(columns) <= 1:
                    return columns

                if self.df is not None:
                    return _columns_original_order(
                        columns_unordered=columns,
                        columns_ordered=nw.from_native(self.df)
                        .lazy()
                        .collect_schema()
                        .names(),
                    )
                else:
                    return columns

        def has_constant(
            self=None, formula: str = "", true_if_missing: bool = False
        ) -> bool:
            """
            Check if formula includes an intercept.

            Parameters
            ----------
            formula : str, optional
                Formula string. If empty, uses self.formula. Default is "".
            true_if_missing : bool, optional
                Return True if constant term is implicit (not specified).
                Default is False.

            Returns
            -------
            bool
                True if formula includes intercept.
            """
            if type(self) is str:
                formula = self
                self = None

            if self is not None:
                if formula == "":
                    formula = self.formula

            #   Separate into subclauses
            sides = formula.split("~")
            if len(sides) == 2:
                lhs = sides[0]
                rhs = sides[1]
            else:
                lhs = None
                rhs = sides[0]

            if true_if_missing:
                return not rhs.replace(" ", "").startswith("0")
            else:
                return rhs.replace(" ", "").startswith("1")

        def remove_constant(self):
            """Remove intercept from formula (change ~1+ to ~0+)."""
            self.formula = self.formula.replace("~1+", "~")
            self.formula = self.formula.replace("~0+", "~")
            self.formula = self.formula.replace("~", "~0+")

        def expand(self=None, formula: str = ""):
            """
            Expand formula shorthand (e.g., a*b becomes a+b+a:b).

            Parameters
            ----------
            formula : str, optional
                Formula string. If empty, uses self.formula. Default is "".

            Returns
            -------
            str
                Expanded formula string.
            """
            if self is not None:
                if formula == "":
                    formula = self.formula
            else:
                self = FormulaBuilder(formula=formula)

            lhs = self.lhs()
            rhs = self.rhs()

            parser = DefaultFormulaParser()
            parsed = parser.get_terms(rhs)
            reconstructed_formula = "+".join([str(i) for i in parsed])

            if lhs != "":
                reconstructed_formula = f"{lhs}~{reconstructed_formula}"

            if self is not None:
                self.formula = reconstructed_formula

            return reconstructed_formula

        def exclude_interactions(
            self=None,
            formula: str = "",
            b_exclude_powers: bool = True,
            df: IntoFrameT | None = None,
        ) -> tuple[str, bool]:
            """
            Remove interaction terms from formula.

            Parameters
            ----------
            formula : str, optional
                Formula string. If empty, uses self.formula. Default is "".
            b_exclude_powers : bool, optional
                Also exclude polynomial terms. Default is True.
            df : IntoFrameT | None, optional
                Reference dataframe. Default is None.

            Returns
            -------
            tuple[str, bool]
                (modified formula, whether any terms were dropped)
            """
            if self is not None:
                if df is None:
                    df = self.df
                if formula == "":
                    formula = self.formula
            else:
                self = FormulaBuilder(df=df, formula=formula)

            #   It's easier with the expanded formula
            self.expand()

            #   Separate into subclauses
            sides = self.formula.split("~")
            if len(sides) == 2:
                lhs = sides[0]
                rhs = sides[1]
            else:
                lhs = ""
                rhs = sides[0]
            subclauses = rhs.split("+")

            rhs = ""

            any_dropped = False

            for clausei in subclauses:
                if b_exclude_powers and "^" in clausei:
                    any_dropped = True
                    logger.info(
                        f"Dropping {clausei} from formula for having a variable to a power"
                    )
                elif ":" in clausei:
                    #   Direct interaction
                    any_dropped = True
                    logger.info(f"Dropping {clausei} from formula")
                else:
                    #   include this in the final formula
                    rhs += f"+{clausei.strip()}"

            #   Get rid of leading +
            rhs = rhs[1 : len(rhs)]

            output = f"{lhs}~{rhs}"

            self.formula = output
            return (output, any_dropped)

        def exclude_variables(
            self=None,
            exclude_list: list = None,
            formula: str = "",
            df: IntoFrameT | None = None,
            case_insensitive: bool = False,
        ):
            """
            Remove specific variables from formula.

            Parameters
            ----------
            exclude_list : list, optional
                Variables to exclude. Default is None.
            formula : str, optional
                Formula string. If empty, uses self.formula. Default is "".
            df : IntoFrameT | None, optional
                Reference dataframe. Default is None.
            case_insensitive : bool, optional
                Case-insensitive matching. Default is False.

            Returns
            -------
            str | FormulaBuilder
                Modified formula string or self for chaining.
            """
            if exclude_list is None:
                exclude_list = []

            if self is not None:
                if df is None:
                    df = self.df
                if formula == "":
                    formula = self.formula

            if df is not None:
                exclude_list = columns_from_list(
                    df=df, columns=exclude_list, case_insensitive=case_insensitive
                )

            #   Separate into subclauses
            sides = formula.split("~")
            if len(sides) == 2:
                lhs = sides[0]
                rhs = sides[1]
            else:
                lhs = None
                rhs = sides[0]
            subclauses = rhs.split("+")

            rhs = ""

            regex_list = [
                f"(^|([^a-zA-Z0-9_])){itemi}($|([^a-zA-Z0-9_]))" for itemi in exclude_list
            ]
            regexes = "(" + ")|(".join(regex_list) + ")"

            for clausei in subclauses:
                if re.match(regexes, clausei) is None:
                    #   include this in the final formula
                    rhs += f"+{clausei}"
                else:
                    logger.info(f"Dropping {clausei} from formula")

            #   Get rid of leading +
            rhs = rhs[1 : len(rhs)]

            if lhs is not None:
                output = f"{lhs}~{rhs}"
            else:
                output = rhs

            if self is None:
                return output
            else:
                self.formula = output
                return self

        def formula_with_varnames_in_brackets(
            self=None,
            clause: str = "",
            df: pl.LazyFrame | pl.DataFrame | None = None,
            case_insensitive: bool = False,
            append: bool = False,
        ) -> str | FormulaBuilder:
            """
            Expand {pattern} placeholders with matching column names.

            Replaces {var*} with all columns matching var* pattern in the dataframe.
            Useful for programmatically building formulas with wildcards.

            Parameters
            ----------
            clause : str, optional
                Formula clause with {pattern} placeholders. Default is "".
            df : pl.LazyFrame | pl.DataFrame | None, optional
                Dataframe for column lookup. Default is None.
            case_insensitive : bool, optional
                Case-insensitive pattern matching. Default is False.
            append : bool, optional
                Append to existing formula instead of replacing.
                Only used when called on instance. Default is False.

            Returns
            -------
            str | FormulaBuilder
                Expanded formula string or self for chaining.

            Examples
            --------
            >>> fb = FormulaBuilder(df=df)
            >>> fb.formula_with_varnames_in_brackets("{income_*} + age")
            >>> print(fb.formula)
            'y~1+income_wages+income_self_employment+age'
            """

            call_recursively = False
            if df is None and self is not None:
                df = self.df

            #   Separate into subclauses
            sides = clause.split("~")
            if len(sides) == 2:
                lhs = sides[0]
                rhs = sides[1]
            else:
                lhs = None
                rhs = sides[0]
            subclauses = rhs.split("+")

            rhs = ""
            for clausei in subclauses:
                replaced_clause = ""

                left_bracket = clausei.find("{")
                right_bracket = clausei.find("}")

                if left_bracket >= 0 and right_bracket >= 0:
                    replace_string = clausei[left_bracket : right_bracket + 1]
                    var_name = replace_string[1 : len(replace_string) - 1]
                    Columns = columns_from_list(
                        df=df, columns=[var_name], case_insensitive=case_insensitive
                    )

                    for coli in Columns:
                        if replaced_clause != "":
                            replaced_clause += "+"
                        replaced_clause += clausei.replace(replace_string, coli)

                    clausei = replaced_clause

                call_recursively = clausei.find("{") >= 0 and clausei.find("}") >= 0
                rhs += f"+{clausei}"

            #   Get rid of leading +
            rhs = rhs[1 : len(rhs)]

            if lhs is not None:
                output = f"{lhs}~{rhs}"
            else:
                output = rhs

            if call_recursively:
                output = FormulaBuilder.formula_with_varnames_in_brackets(
                    clause=output, df=df, case_insensitive=case_insensitive
                )

            if self is None:
                return output
            else:
                if append:
                    self.add_to_formula(output)
                else:
                    self.formula = output
                return self

### columns `property`

    columns

Get all variables required by the formula.

### columns_lhs `property`

    columns_lhs

Variables in left-hand side, ordered as in dataframe.

### columns_rhs `property`

    columns_rhs

Variables in right-hand side, ordered as in dataframe.

### add_to_formula

    add_to_formula(
        add_part: str = "", plus_first: bool = True
    ) -> None

Append a string to the formula.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`add_part` | `str` | String to append. Default is "". | `''`
`plus_first` | `bool` | Add "+" before the string. Default is True. | `True`

Source code in `src\survey_kit\utilities\formula_builder.py`

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    def add_to_formula(self, add_part: str = "", plus_first: bool = True) -> None:
        """
        Append a string to the formula.

        Parameters
        ----------
        add_part : str, optional
            String to append. Default is "".
        plus_first : bool, optional
            Add "+" before the string. Default is True.
        """
        if plus_first:
            plus = "+"
        else:
            plus = ""
        self.formula += f"{plus}{add_part}"

### any_wrapper

    any_wrapper(
        df: IntoFrameT | None = None,
        columns: str | list | None = None,
        clause: str = "",
        case_insensitive: bool = False,
        prefix: str = "",
        suffix: str = "",
    ) -> str | FormulaBuilder

General wrapper for adding columns with prefix/suffix.

Used internally by other methods to add variables with transformations.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`df` | `IntoFrameT | None` | Dataframe for resolving column patterns. Default is None. | `None`
`columns` | `str | list | None` | Column names or patterns. Default is None. | `None`
`clause` | `str` | Pre-constructed clause (bypasses column lookup). Default is "". | `''`
`case_insensitive` | `bool` | Case-insensitive column matching. Default is False. | `False`
`prefix` | `str` | String to prepend to each column. Default is "". | `''`
`suffix` | `str` | String to append to each column. Default is "". | `''`

Returns:

Type | Description
--- | ---
`str | FormulaBuilder` | Formula string if self is None, otherwise returns self for chaining.

Source code in `src\survey_kit\utilities\formula_builder.py`

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    def any_wrapper(
        self=None,
        df: IntoFrameT | None = None,
        columns: str | list | None = None,
        clause: str = "",
        case_insensitive: bool = False,
        prefix: str = "",
        suffix: str = "",
    ) -> str | FormulaBuilder:
        """
        General wrapper for adding columns with prefix/suffix.

        Used internally by other methods to add variables with transformations.

        Parameters
        ----------
        df : IntoFrameT | None, optional
            Dataframe for resolving column patterns. Default is None.
        columns : str | list | None, optional
            Column names or patterns. Default is None.
        clause : str, optional
            Pre-constructed clause (bypasses column lookup). Default is "".
        case_insensitive : bool, optional
            Case-insensitive column matching. Default is False.
        prefix : str, optional
            String to prepend to each column. Default is "".
        suffix : str, optional
            String to append to each column. Default is "".

        Returns
        -------
        str | FormulaBuilder
            Formula string if self is None, otherwise returns self for chaining.
        """
        columns = list_input(columns)

        #   Dataframe to look for columns in
        if df is None:
            df = self.df

        if clause != "":
            output = f"+{prefix}{clause}{suffix}"
        else:
            if df is not None:
                columns = columns_from_list(
                    df=df, columns=columns, case_insensitive=case_insensitive
                )

            out_list = [f"{prefix}{coli}{suffix}" for coli in columns]
            output = "+" + "+".join(out_list)

        if self is None:
            return output
        else:
            self.formula += output
            return self

### center

    center(
        df: IntoFrameT | None = None,
        columns: str | list | None = None,
        clause: str = "",
        case_insensitive: bool = False,
    ) -> str | FormulaBuilder

Center variables (subtract mean).

Convenience method for scale(standardize=False).

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`df` | `IntoFrameT | None` | Dataframe for column lookup. Default is None. | `None`
`columns` | `str | list | None` | Column names or patterns. Default is None. | `None`
`clause` | `str` | Pre-constructed clause. Default is "". | `''`
`case_insensitive` | `bool` | Case-insensitive matching. Default is False. | `False`

Returns:

Type | Description
--- | ---
`str | FormulaBuilder` | Formula string or self for chaining.

Source code in `src\survey_kit\utilities\formula_builder.py`

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    def center(
        self=None,
        df: IntoFrameT | None = None,
        columns: str | list | None = None,
        clause: str = "",
        case_insensitive: bool = False,
    ) -> str | FormulaBuilder:
        """
        Center variables (subtract mean).

        Convenience method for scale(standardize=False).

        Parameters
        ----------
        df : IntoFrameT | None, optional
            Dataframe for column lookup. Default is None.
        columns : str | list | None, optional
            Column names or patterns. Default is None.
        clause : str, optional
            Pre-constructed clause. Default is "".
        case_insensitive : bool, optional
            Case-insensitive matching. Default is False.

        Returns
        -------
        str | FormulaBuilder
            Formula string or self for chaining.
        """
        if self is None:
            caller = FormulaBuilder
        else:
            caller = self

        return caller.scale(
            df=df,
            columns=columns,
            clause=clause,
            case_insensitive=case_insensitive,
            standardize=False,
        )

### columns_from_formula

    columns_from_formula(formula: str = '') -> list[str]

Extract variable names from a formula.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`formula` | `str` | Formula string. If empty, uses self.formula. Default is "". | `''`

Returns:

Type | Description
--- | ---
`list[str]` | Variable names required by the formula.

Source code in `src\survey_kit\utilities\formula_builder.py`

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    def columns_from_formula(self=None, formula: str = "") -> list[str]:
        """
        Extract variable names from a formula.

        Parameters
        ----------
        formula : str, optional
            Formula string. If empty, uses self.formula. Default is "".

        Returns
        -------
        list[str]
            Variable names required by the formula.
        """
        if type(self) is str:
            formula = self
            self = None

        if self is not None and formula == "":
            formula = self.formula
        return list(Formula(formula).required_variables)

### continuous

    continuous(
        df: IntoFrameT | None = None,
        columns: str | list | None = None,
        clause: str = "",
        case_insensitive: bool = False,
    ) -> str | FormulaBuilder

Add continuous variables to the formula.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`df` | `IntoFrameT | None` | Dataframe for column lookup. Default is None. | `None`
`columns` | `str | list | None` | Column names or patterns (e.g., "income_*"). Default is None. | `None`
`clause` | `str` | Pre-constructed clause. Default is "". | `''`
`case_insensitive` | `bool` | Case-insensitive matching. Default is False. | `False`

Returns:

Type | Description
--- | ---
`str | FormulaBuilder` | Formula string or self for chaining.

Examples:

    >>> fb = FormulaBuilder(df=df, lhs="y")
    >>> fb.continuous(columns=["age", "income"])
    >>> print(fb.formula)
    'y~1+age+income'

Source code in `src\survey_kit\utilities\formula_builder.py`

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    def continuous(
        self=None,
        df: IntoFrameT | None = None,
        columns: str | list | None = None,
        clause: str = "",
        case_insensitive: bool = False,
    ) -> str | FormulaBuilder:
        """
        Add continuous variables to the formula.

        Parameters
        ----------
        df : IntoFrameT | None, optional
            Dataframe for column lookup. Default is None.
        columns : str | list | None, optional
            Column names or patterns (e.g., "income_*"). Default is None.
        clause : str, optional
            Pre-constructed clause. Default is "".
        case_insensitive : bool, optional
            Case-insensitive matching. Default is False.

        Returns
        -------
        str | FormulaBuilder
            Formula string or self for chaining.

        Examples
        --------
        >>> fb = FormulaBuilder(df=df, lhs="y")
        >>> fb.continuous(columns=["age", "income"])
        >>> print(fb.formula)
        'y~1+age+income'
        """
        if self is None:
            caller = FormulaBuilder
        else:
            caller = self

        return caller.any_wrapper(
            df=df, columns=columns, clause=clause, case_insensitive=case_insensitive
        )

### exclude_interactions

    exclude_interactions(
        formula: str = "",
        b_exclude_powers: bool = True,
        df: IntoFrameT | None = None,
    ) -> tuple[str, bool]

Remove interaction terms from formula.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`formula` | `str` | Formula string. If empty, uses self.formula. Default is "". | `''`
`b_exclude_powers` | `bool` | Also exclude polynomial terms. Default is True. | `True`
`df` | `IntoFrameT | None` | Reference dataframe. Default is None. | `None`

Returns:

Type | Description
--- | ---
`tuple[str, bool]` | (modified formula, whether any terms were dropped)

Source code in `src\survey_kit\utilities\formula_builder.py`

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    def exclude_interactions(
        self=None,
        formula: str = "",
        b_exclude_powers: bool = True,
        df: IntoFrameT | None = None,
    ) -> tuple[str, bool]:
        """
        Remove interaction terms from formula.

        Parameters
        ----------
        formula : str, optional
            Formula string. If empty, uses self.formula. Default is "".
        b_exclude_powers : bool, optional
            Also exclude polynomial terms. Default is True.
        df : IntoFrameT | None, optional
            Reference dataframe. Default is None.

        Returns
        -------
        tuple[str, bool]
            (modified formula, whether any terms were dropped)
        """
        if self is not None:
            if df is None:
                df = self.df
            if formula == "":
                formula = self.formula
        else:
            self = FormulaBuilder(df=df, formula=formula)

        #   It's easier with the expanded formula
        self.expand()

        #   Separate into subclauses
        sides = self.formula.split("~")
        if len(sides) == 2:
            lhs = sides[0]
            rhs = sides[1]
        else:
            lhs = ""
            rhs = sides[0]
        subclauses = rhs.split("+")

        rhs = ""

        any_dropped = False

        for clausei in subclauses:
            if b_exclude_powers and "^" in clausei:
                any_dropped = True
                logger.info(
                    f"Dropping {clausei} from formula for having a variable to a power"
                )
            elif ":" in clausei:
                #   Direct interaction
                any_dropped = True
                logger.info(f"Dropping {clausei} from formula")
            else:
                #   include this in the final formula
                rhs += f"+{clausei.strip()}"

        #   Get rid of leading +
        rhs = rhs[1 : len(rhs)]

        output = f"{lhs}~{rhs}"

        self.formula = output
        return (output, any_dropped)

### exclude_variables

    exclude_variables(
        exclude_list: list = None,
        formula: str = "",
        df: IntoFrameT | None = None,
        case_insensitive: bool = False,
    )

Remove specific variables from formula.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`exclude_list` | `list` | Variables to exclude. Default is None. | `None`
`formula` | `str` | Formula string. If empty, uses self.formula. Default is "". | `''`
`df` | `IntoFrameT | None` | Reference dataframe. Default is None. | `None`
`case_insensitive` | `bool` | Case-insensitive matching. Default is False. | `False`

Returns:

Type | Description
--- | ---
`str | FormulaBuilder` | Modified formula string or self for chaining.

Source code in `src\survey_kit\utilities\formula_builder.py`

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    def exclude_variables(
        self=None,
        exclude_list: list = None,
        formula: str = "",
        df: IntoFrameT | None = None,
        case_insensitive: bool = False,
    ):
        """
        Remove specific variables from formula.

        Parameters
        ----------
        exclude_list : list, optional
            Variables to exclude. Default is None.
        formula : str, optional
            Formula string. If empty, uses self.formula. Default is "".
        df : IntoFrameT | None, optional
            Reference dataframe. Default is None.
        case_insensitive : bool, optional
            Case-insensitive matching. Default is False.

        Returns
        -------
        str | FormulaBuilder
            Modified formula string or self for chaining.
        """
        if exclude_list is None:
            exclude_list = []

        if self is not None:
            if df is None:
                df = self.df
            if formula == "":
                formula = self.formula

        if df is not None:
            exclude_list = columns_from_list(
                df=df, columns=exclude_list, case_insensitive=case_insensitive
            )

        #   Separate into subclauses
        sides = formula.split("~")
        if len(sides) == 2:
            lhs = sides[0]
            rhs = sides[1]
        else:
            lhs = None
            rhs = sides[0]
        subclauses = rhs.split("+")

        rhs = ""

        regex_list = [
            f"(^|([^a-zA-Z0-9_])){itemi}($|([^a-zA-Z0-9_]))" for itemi in exclude_list
        ]
        regexes = "(" + ")|(".join(regex_list) + ")"

        for clausei in subclauses:
            if re.match(regexes, clausei) is None:
                #   include this in the final formula
                rhs += f"+{clausei}"
            else:
                logger.info(f"Dropping {clausei} from formula")

        #   Get rid of leading +
        rhs = rhs[1 : len(rhs)]

        if lhs is not None:
            output = f"{lhs}~{rhs}"
        else:
            output = rhs

        if self is None:
            return output
        else:
            self.formula = output
            return self

### expand

    expand(formula: str = '')

Expand formula shorthand (e.g., a*b becomes a+b+a:b).

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`formula` | `str` | Formula string. If empty, uses self.formula. Default is "". | `''`

Returns:

Type | Description
--- | ---
`str` | Expanded formula string.

Source code in `src\survey_kit\utilities\formula_builder.py`

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    def expand(self=None, formula: str = ""):
        """
        Expand formula shorthand (e.g., a*b becomes a+b+a:b).

        Parameters
        ----------
        formula : str, optional
            Formula string. If empty, uses self.formula. Default is "".

        Returns
        -------
        str
            Expanded formula string.
        """
        if self is not None:
            if formula == "":
                formula = self.formula
        else:
            self = FormulaBuilder(formula=formula)

        lhs = self.lhs()
        rhs = self.rhs()

        parser = DefaultFormulaParser()
        parsed = parser.get_terms(rhs)
        reconstructed_formula = "+".join([str(i) for i in parsed])

        if lhs != "":
            reconstructed_formula = f"{lhs}~{reconstructed_formula}"

        if self is not None:
            self.formula = reconstructed_formula

        return reconstructed_formula

### factor

    factor(
        df: IntoFrameT | None = None,
        columns: str | list | None = None,
        clause: str = "",
        reference=None,
        case_insensitive: bool = False,
    )

Add categorical variables with treatment coding.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`df` | `IntoFrameT | None` | Dataframe for column lookup. Default is None. | `None`
`columns` | `str | list | None` | Column names or patterns. Default is None. | `None`
`clause` | `str` | Pre-constructed clause. Default is "". | `''`
`reference` | `str | int | None` | Reference level for treatment coding. Default is None (use first level). | `None`
`case_insensitive` | `bool` | Case-insensitive matching. Default is False. | `False`

Returns:

Type | Description
--- | ---
`str | FormulaBuilder` | Formula string or self for chaining.

Examples:

    >>> fb.factor(columns="region")
    >>> print(fb.formula)
    'y~1+C(region)'

    >>> fb.factor(columns="education", reference="high_school")
    >>> print(fb.formula)
    "y~1+C(education, contr.treatment('high_school'))"

Source code in `src\survey_kit\utilities\formula_builder.py`

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    def factor(
        self=None,
        df: IntoFrameT | None = None,
        columns: str | list | None = None,
        clause: str = "",
        reference=None,
        case_insensitive: bool = False,
    ):
        """
        Add categorical variables with treatment coding.

        Parameters
        ----------
        df : IntoFrameT | None, optional
            Dataframe for column lookup. Default is None.
        columns : str | list | None, optional
            Column names or patterns. Default is None.
        clause : str, optional
            Pre-constructed clause. Default is "".
        reference : str | int | None, optional
            Reference level for treatment coding. Default is None (use first level).
        case_insensitive : bool, optional
            Case-insensitive matching. Default is False.

        Returns
        -------
        str | FormulaBuilder
            Formula string or self for chaining.

        Examples
        --------
        >>> fb.factor(columns="region")
        >>> print(fb.formula)
        'y~1+C(region)'

        >>> fb.factor(columns="education", reference="high_school")
        >>> print(fb.formula)
        "y~1+C(education, contr.treatment('high_school'))"
        """
        if self is None:
            caller = FormulaBuilder
        else:
            caller = self

        prefix = "C("
        if reference is not None:
            if type(reference) is str:
                suffix = f", contr.treatment('{reference}'))"
            else:
                suffix = f", contr.treatment({reference}))"

        else:
            suffix = ")"  #  f", contr.treatment)"

        return caller.any_wrapper(
            df=df, columns=columns, clause=clause, prefix=prefix, suffix=suffix
        )

### formula_with_varnames_in_brackets

    formula_with_varnames_in_brackets(
        clause: str = "",
        df: LazyFrame | DataFrame | None = None,
        case_insensitive: bool = False,
        append: bool = False,
    ) -> str | FormulaBuilder

Expand {pattern} placeholders with matching column names.

Replaces {var} with all columns matching var pattern in the dataframe. Useful for programmatically building formulas with wildcards.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`clause` | `str` | Formula clause with {pattern} placeholders. Default is "". | `''`
`df` | `LazyFrame | DataFrame | None` | Dataframe for column lookup. Default is None. | `None`
`case_insensitive` | `bool` | Case-insensitive pattern matching. Default is False. | `False`
`append` | `bool` | Append to existing formula instead of replacing. Only used when called on instance. Default is False. | `False`

Returns:

Type | Description
--- | ---
`str | FormulaBuilder` | Expanded formula string or self for chaining.

Examples:

    >>> fb = FormulaBuilder(df=df)
    >>> fb.formula_with_varnames_in_brackets("{income_*} + age")
    >>> print(fb.formula)
    'y~1+income_wages+income_self_employment+age'

Source code in `src\survey_kit\utilities\formula_builder.py`

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    def formula_with_varnames_in_brackets(
        self=None,
        clause: str = "",
        df: pl.LazyFrame | pl.DataFrame | None = None,
        case_insensitive: bool = False,
        append: bool = False,
    ) -> str | FormulaBuilder:
        """
        Expand {pattern} placeholders with matching column names.

        Replaces {var*} with all columns matching var* pattern in the dataframe.
        Useful for programmatically building formulas with wildcards.

        Parameters
        ----------
        clause : str, optional
            Formula clause with {pattern} placeholders. Default is "".
        df : pl.LazyFrame | pl.DataFrame | None, optional
            Dataframe for column lookup. Default is None.
        case_insensitive : bool, optional
            Case-insensitive pattern matching. Default is False.
        append : bool, optional
            Append to existing formula instead of replacing.
            Only used when called on instance. Default is False.

        Returns
        -------
        str | FormulaBuilder
            Expanded formula string or self for chaining.

        Examples
        --------
        >>> fb = FormulaBuilder(df=df)
        >>> fb.formula_with_varnames_in_brackets("{income_*} + age")
        >>> print(fb.formula)
        'y~1+income_wages+income_self_employment+age'
        """

        call_recursively = False
        if df is None and self is not None:
            df = self.df

        #   Separate into subclauses
        sides = clause.split("~")
        if len(sides) == 2:
            lhs = sides[0]
            rhs = sides[1]
        else:
            lhs = None
            rhs = sides[0]
        subclauses = rhs.split("+")

        rhs = ""
        for clausei in subclauses:
            replaced_clause = ""

            left_bracket = clausei.find("{")
            right_bracket = clausei.find("}")

            if left_bracket >= 0 and right_bracket >= 0:
                replace_string = clausei[left_bracket : right_bracket + 1]
                var_name = replace_string[1 : len(replace_string) - 1]
                Columns = columns_from_list(
                    df=df, columns=[var_name], case_insensitive=case_insensitive
                )

                for coli in Columns:
                    if replaced_clause != "":
                        replaced_clause += "+"
                    replaced_clause += clausei.replace(replace_string, coli)

                clausei = replaced_clause

            call_recursively = clausei.find("{") >= 0 and clausei.find("}") >= 0
            rhs += f"+{clausei}"

        #   Get rid of leading +
        rhs = rhs[1 : len(rhs)]

        if lhs is not None:
            output = f"{lhs}~{rhs}"
        else:
            output = rhs

        if call_recursively:
            output = FormulaBuilder.formula_with_varnames_in_brackets(
                clause=output, df=df, case_insensitive=case_insensitive
            )

        if self is None:
            return output
        else:
            if append:
                self.add_to_formula(output)
            else:
                self.formula = output
            return self

### function

    function(
        df: IntoFrameT | None = None,
        columns: str | list | None = None,
        clause: str = "",
        operator_before: str = "",
        operator_after: str = "",
        function_item: str = "",
        case_insensitive: bool = False,
        **kwargs,
    ) -> str | FormulaBuilder

Wrap columns in a function call.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`df` | `IntoFrameT | None` | Dataframe for column lookup. Default is None. | `None`
`columns` | `str | list | None` | Column names or patterns. Default is None. | `None`
`clause` | `str` | Pre-constructed clause. Default is "". | `''`
`operator_before` | `str` | String before function call. Default is "". | `''`
`operator_after` | `str` | String after function call. Default is "". | `''`
`function_item` | `str` | Function name (e.g., "log", "sqrt"). Default is "". | `''`
`case_insensitive` | `bool` | Case-insensitive matching. Default is False. | `False`
`**kwargs` |  | Additional function arguments. | `{}`

Returns:

Type | Description
--- | ---
`str | FormulaBuilder` | Formula string or self for chaining.

Examples:

    >>> fb.function(columns="income", function_item="log")
    >>> print(fb.formula)
    'y~1+log(income)'

Source code in `src\survey_kit\utilities\formula_builder.py`

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    def function(
        self=None,
        df: IntoFrameT | None = None,
        columns: str | list | None = None,
        clause: str = "",
        operator_before: str = "",
        operator_after: str = "",
        function_item: str = "",
        case_insensitive: bool = False,
        **kwargs,
    ) -> str | FormulaBuilder:
        """
        Wrap columns in a function call.

        Parameters
        ----------
        df : IntoFrameT | None, optional
            Dataframe for column lookup. Default is None.
        columns : str | list | None, optional
            Column names or patterns. Default is None.
        clause : str, optional
            Pre-constructed clause. Default is "".
        operator_before : str, optional
            String before function call. Default is "".
        operator_after : str, optional
            String after function call. Default is "".
        function_item : str, optional
            Function name (e.g., "log", "sqrt"). Default is "".
        case_insensitive : bool, optional
            Case-insensitive matching. Default is False.
        **kwargs
            Additional function arguments.

        Returns
        -------
        str | FormulaBuilder
            Formula string or self for chaining.

        Examples
        --------
        >>> fb.function(columns="income", function_item="log")
        >>> print(fb.formula)
        'y~1+log(income)'
        """
        if self is None:
            caller = FormulaBuilder
        else:
            caller = self

        if function_item != "":
            operator_before = f"{operator_before}{function_item}("

            operator_after_final = ""
            if len(kwargs):
                for keyi, valuei in kwargs.items():
                    operator_after_final += ","
                    if type(valuei) is str:
                        operator_after_final += f"{keyi}='{valuei}'"
                    else:
                        operator_after_final += f"{keyi}={valuei}"

            operator_after_final += f"{operator_after})"
        else:
            operator_after_final = operator_after

        return caller.any_wrapper(
            df=df,
            columns=columns,
            clause=clause,
            case_insensitive=case_insensitive,
            prefix=f"{{{operator_before}",
            suffix=f"{operator_after_final}}}",
        )

### has_constant

    has_constant(
        formula: str = "", true_if_missing: bool = False
    ) -> bool

Check if formula includes an intercept.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`formula` | `str` | Formula string. If empty, uses self.formula. Default is "". | `''`
`true_if_missing` | `bool` | Return True if constant term is implicit (not specified). Default is False. | `False`

Returns:

Type | Description
--- | ---
`bool` | True if formula includes intercept.

Source code in `src\survey_kit\utilities\formula_builder.py`

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    def has_constant(
        self=None, formula: str = "", true_if_missing: bool = False
    ) -> bool:
        """
        Check if formula includes an intercept.

        Parameters
        ----------
        formula : str, optional
            Formula string. If empty, uses self.formula. Default is "".
        true_if_missing : bool, optional
            Return True if constant term is implicit (not specified).
            Default is False.

        Returns
        -------
        bool
            True if formula includes intercept.
        """
        if type(self) is str:
            formula = self
            self = None

        if self is not None:
            if formula == "":
                formula = self.formula

        #   Separate into subclauses
        sides = formula.split("~")
        if len(sides) == 2:
            lhs = sides[0]
            rhs = sides[1]
        else:
            lhs = None
            rhs = sides[0]

        if true_if_missing:
            return not rhs.replace(" ", "").startswith("0")
        else:
            return rhs.replace(" ", "").startswith("1")

### interact_clauses

    interact_clauses(
        clause1: str = "",
        clause2: str = "",
        no_base: bool = False,
    ) -> str | FormulaBuilder

Create interactions between two formula clauses.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`clause1` | `str` | First formula clause. Default is "". | `''`
`clause2` | `str` | Second formula clause. Default is "". | `''`
`no_base` | `bool` | Exclude main effects from clauses. Default is False. | `False`

Returns:

Type | Description
--- | ---
`str | FormulaBuilder` | Formula string or self for chaining.

Examples:

    >>> fb.interact_clauses("age+education", "region")
    >>> print(fb.formula)
    'y~1+(age+education)*(region)'

Source code in `src\survey_kit\utilities\formula_builder.py`

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    def interact_clauses(
        self=None, clause1: str = "", clause2: str = "", no_base: bool = False
    ) -> str | FormulaBuilder:
        """
        Create interactions between two formula clauses.

        Parameters
        ----------
        clause1 : str, optional
            First formula clause. Default is "".
        clause2 : str, optional
            Second formula clause. Default is "".
        no_base : bool, optional
            Exclude main effects from clauses. Default is False.

        Returns
        -------
        str | FormulaBuilder
            Formula string or self for chaining.

        Examples
        --------
        >>> fb.interact_clauses("age+education", "region")
        >>> print(fb.formula)
        'y~1+(age+education)*(region)'
        """
        if clause1.startswith("+"):
            clause1 = clause1[1:]
        if clause2.startswith("+"):
            clause2 = clause2[1:]

        clause1 = clause1.replace("++", "+")
        clause2 = clause2.replace("++", "+")

        output = f"({clause1})*({clause2})"

        if no_base:
            output += f"-({clause1} + {clause2})"

        if self is None:
            return f"+{output}"
        else:
            self.add_to_formula(output)
            return self

### lhs

    lhs(formula: str = '') -> str

Get left-hand side of formula.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`formula` | `str` | Formula string. If empty, uses self.formula. Default is "". | `''`

Returns:

Type | Description
--- | ---
`str` | Left-hand side (response variable).

Source code in `src\survey_kit\utilities\formula_builder.py`

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    def lhs(self=None, formula: str = "") -> str:
        """
        Get left-hand side of formula.

        Parameters
        ----------
        formula : str, optional
            Formula string. If empty, uses self.formula. Default is "".

        Returns
        -------
        str
            Left-hand side (response variable).
        """
        if type(self) is str:
            formula = self
            self = None

        if self is not None:
            if formula == "":
                formula = self.formula

        #   Separate into subclauses
        sides = formula.split("~")

        if len(sides) == 2:
            lhs_string = sides[0]
        else:
            lhs_string = ""

        return lhs_string

### polynomial

    polynomial(
        df: IntoFrameT | None = None,
        columns: str | list | None = None,
        clause: str = "",
        degree: int = 0,
        case_insensitive: bool = False,
        center: bool = False,
    ) -> str | FormulaBuilder

Add polynomial terms.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`df` | `IntoFrameT | None` | Dataframe for column lookup. Default is None. | `None`
`columns` | `str | list | None` | Column names or patterns. Default is None. | `None`
`clause` | `str` | Pre-constructed clause. Default is "". | `''`
`degree` | `int` | Polynomial degree. Default is 0 (returns continuous). | `0`
`case_insensitive` | `bool` | Case-insensitive matching. Default is False. | `False`
`center` | `bool` | Use orthogonal polynomials (centered). Default is False (raw polynomials). | `False`

Returns:

Type | Description
--- | ---
`str | FormulaBuilder` | Formula string or self for chaining.

Examples:

    >>> fb.polynomial(columns="age", degree=3)
    >>> print(fb.formula)
    'y~1+poly(age,degree=3,raw=True)'

    >>> fb.polynomial(columns="age", degree=2, center=True)
    >>> print(fb.formula)
    'y~1+poly(age,degree=2,raw=False)'

Source code in `src\survey_kit\utilities\formula_builder.py`

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    def polynomial(
        self=None,
        df: IntoFrameT | None = None,
        columns: str | list | None = None,
        clause: str = "",
        degree: int = 0,
        case_insensitive: bool = False,
        center: bool = False,
    ) -> str | FormulaBuilder:
        """
        Add polynomial terms.

        Parameters
        ----------
        df : IntoFrameT | None, optional
            Dataframe for column lookup. Default is None.
        columns : str | list | None, optional
            Column names or patterns. Default is None.
        clause : str, optional
            Pre-constructed clause. Default is "".
        degree : int, optional
            Polynomial degree. Default is 0 (returns continuous).
        case_insensitive : bool, optional
            Case-insensitive matching. Default is False.
        center : bool, optional
            Use orthogonal polynomials (centered). Default is False (raw polynomials).

        Returns
        -------
        str | FormulaBuilder
            Formula string or self for chaining.

        Examples
        --------
        >>> fb.polynomial(columns="age", degree=3)
        >>> print(fb.formula)
        'y~1+poly(age,degree=3,raw=True)'

        >>> fb.polynomial(columns="age", degree=2, center=True)
        >>> print(fb.formula)
        'y~1+poly(age,degree=2,raw=False)'
        """
        if self is None:
            caller = FormulaBuilder
        else:
            caller = self
            if df is None:
                df = self.df

        if degree <= 1:
            if center:
                return caller.center(
                    df=df,
                    columns=columns,
                    clause=clause,
                    case_insensitive=case_insensitive,
                )
            else:
                return caller.continuous(
                    df=df,
                    columns=columns,
                    clause=clause,
                    case_insensitive=case_insensitive,
                )
        else:
            subformula = ""

            for power in range(1, degree + 1):
                operator_before = "poly("
                if center:
                    operator_after = f",degree={degree},raw=False)"
                else:
                    operator_after = f",degree={degree},raw=True)"

                subformula += FormulaBuilder.function(
                    df=df,
                    columns=columns,
                    clause=clause,
                    operator_before=operator_before,
                    operator_after=operator_after,
                    case_insensitive=case_insensitive,
                )

            if self is None:
                return subformula
            else:
                self.add_to_formula(subformula, False)
                return self

### remove_constant

    remove_constant()

Remove intercept from formula (change ~1+ to ~0+).

Source code in `src\survey_kit\utilities\formula_builder.py`

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    def remove_constant(self):
        """Remove intercept from formula (change ~1+ to ~0+)."""
        self.formula = self.formula.replace("~1+", "~")
        self.formula = self.formula.replace("~0+", "~")
        self.formula = self.formula.replace("~", "~0+")

### rhs

    rhs(formula: str = '') -> str

Get right-hand side of formula.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`formula` | `str` | Formula string. If empty, uses self.formula. Default is "". | `''`

Returns:

Type | Description
--- | ---
`str` | Right-hand side (predictors).

Source code in `src\survey_kit\utilities\formula_builder.py`

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    def rhs(self=None, formula: str = "") -> str:
        """
        Get right-hand side of formula.

        Parameters
        ----------
        formula : str, optional
            Formula string. If empty, uses self.formula. Default is "".

        Returns
        -------
        str
            Right-hand side (predictors).
        """
        if type(self) is str:
            formula = self
            self = None

        if self is not None:
            if formula == "":
                formula = self.formula

        #   Separate into subclauses
        sides = formula.split("~")

        if len(sides) == 2:
            rhs_string = sides[1]
        else:
            rhs_string = sides[0]

        return rhs_string

### scale

    scale(
        df: IntoFrameT | None = None,
        columns: str | list | None = None,
        clause: str = "",
        standardize: bool = True,
        case_insensitive: bool = False,
    ) -> str | FormulaBuilder

Standardize or center variables.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`df` | `IntoFrameT | None` | Dataframe for column lookup. Default is None. | `None`
`columns` | `str | list | None` | Column names or patterns. Default is None. | `None`
`clause` | `str` | Pre-constructed clause. Default is "". | `''`
`standardize` | `bool` | If True, standardize (mean=0, sd=1). If False, only center (mean=0). Default is True. | `True`
`case_insensitive` | `bool` | Case-insensitive matching. Default is False. | `False`

Returns:

Type | Description
--- | ---
`str | FormulaBuilder` | Formula string or self for chaining.

Examples:

    >>> fb.scale(columns="age")  # Standardize
    >>> fb.scale(columns="income", standardize=False)  # Center only

Source code in `src\survey_kit\utilities\formula_builder.py`

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    def scale(
        self=None,
        df: IntoFrameT | None = None,
        columns: str | list | None = None,
        clause: str = "",
        standardize: bool = True,
        case_insensitive: bool = False,
    ) -> str | FormulaBuilder:
        """
        Standardize or center variables.

        Parameters
        ----------
        df : IntoFrameT | None, optional
            Dataframe for column lookup. Default is None.
        columns : str | list | None, optional
            Column names or patterns. Default is None.
        clause : str, optional
            Pre-constructed clause. Default is "".
        standardize : bool, optional
            If True, standardize (mean=0, sd=1). If False, only center (mean=0).
            Default is True.
        case_insensitive : bool, optional
            Case-insensitive matching. Default is False.

        Returns
        -------
        str | FormulaBuilder
            Formula string or self for chaining.

        Examples
        --------
        >>> fb.scale(columns="age")  # Standardize
        >>> fb.scale(columns="income", standardize=False)  # Center only
        """
        if self is None:
            caller = FormulaBuilder
        else:
            caller = self

        if standardize:
            function_item = "scale"
        else:
            function_item = "center"

        return caller.any_wrapper(
            df=df,
            columns=columns,
            clause=clause,
            case_insensitive=case_insensitive,
            prefix=f"{function_item}(",
            suffix=")",
        )

### simple_interaction

    simple_interaction(
        df: IntoFrameT | None = None,
        columns: str | list | None = None,
        order: int = 2,
        case_insensitive: bool = False,
        sub_function: Callable | None = None,
        no_base: bool = False,
    ) -> str | FormulaBuilder

Create interactions between variables.

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`df` | `IntoFrameT | None` | Dataframe for column lookup. Default is None. | `None`
`columns` | `str | list | None` | Column names or patterns. Default is None. | `None`
`order` | `int` | Interaction order. 2 = pairwise, 3 = three-way, etc. Default is 2. | `2`
`case_insensitive` | `bool` | Case-insensitive matching. Default is False. | `False`
`sub_function` | `Callable | None` | Function to apply to columns before interacting. Default is None. | `None`
`no_base` | `bool` | Exclude main effects (only interactions). Default is False. | `False`

Returns:

Type | Description
--- | ---
`str | FormulaBuilder` | Formula string or self for chaining.

Examples:

    >>> fb.simple_interaction(columns=["age", "education"], order=2)
    >>> print(fb.formula)
    'y~1+(age+education)**2'

    >>> fb.simple_interaction(columns=["a", "b", "c"], order=2, no_base=True)
    >>> print(fb.formula)
    'y~1+(a+b+c)**2-(a+b+c)'  # Interactions only

Source code in `src\survey_kit\utilities\formula_builder.py`

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    def simple_interaction(
        self=None,
        df: IntoFrameT | None = None,
        columns: str | list | None = None,
        order: int = 2,
        case_insensitive: bool = False,
        sub_function: Callable | None = None,
        no_base: bool = False,
    ) -> str | FormulaBuilder:
        """
        Create interactions between variables.

        Parameters
        ----------
        df : IntoFrameT | None, optional
            Dataframe for column lookup. Default is None.
        columns : str | list | None, optional
            Column names or patterns. Default is None.
        order : int, optional
            Interaction order. 2 = pairwise, 3 = three-way, etc. Default is 2.
        case_insensitive : bool, optional
            Case-insensitive matching. Default is False.
        sub_function : Callable | None, optional
            Function to apply to columns before interacting. Default is None.
        no_base : bool, optional
            Exclude main effects (only interactions). Default is False.

        Returns
        -------
        str | FormulaBuilder
            Formula string or self for chaining.

        Examples
        --------
        >>> fb.simple_interaction(columns=["age", "education"], order=2)
        >>> print(fb.formula)
        'y~1+(age+education)**2'

        >>> fb.simple_interaction(columns=["a", "b", "c"], order=2, no_base=True)
        >>> print(fb.formula)
        'y~1+(a+b+c)**2-(a+b+c)'  # Interactions only
        """
        if self is not None:
            if df is None:
                df = self.df

        if sub_function is None:
            sub_function = FormulaBuilder.continuous

        columns = columns_from_list(
            df=df, columns=columns, case_insensitive=case_insensitive
        )

        subformula = sub_function(
            df=df, columns=columns, case_insensitive=case_insensitive
        )
        #   Remove leading plus sign
        subformula = subformula[1 : len(subformula)]

        if len(columns) > 1:
            output = f"({subformula})**{order}"
        else:
            output = subformula

        if no_base:
            output += f"-({subformula})"

        if self is None:
            return f"+{output}"
        else:
            self.add_to_formula(output)
            return self

### standardize

    standardize(
        df: IntoFrameT | None = None,
        columns: str | list | None = None,
        clause: str = "",
        case_insensitive: bool = False,
    ) -> str | FormulaBuilder

Standardize variables (mean=0, sd=1).

Convenience method for scale(standardize=True).

Parameters:

Name | Type | Description | Default
--- | --- | --- | ---
`df` | `IntoFrameT | None` | Dataframe for column lookup. Default is None. | `None`
`columns` | `str | list | None` | Column names or patterns. Default is None. | `None`
`clause` | `str` | Pre-constructed clause. Default is "". | `''`
`case_insensitive` | `bool` | Case-insensitive matching. Default is False. | `False`

Returns:

Type | Description
--- | ---
`str | FormulaBuilder` | Formula string or self for chaining.

Source code in `src\survey_kit\utilities\formula_builder.py`

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    def standardize(
        self=None,
        df: IntoFrameT | None = None,
        columns: str | list | None = None,
        clause: str = "",
        case_insensitive: bool = False,
    ) -> str | FormulaBuilder:
        """
        Standardize variables (mean=0, sd=1).

        Convenience method for scale(standardize=True).

        Parameters
        ----------
        df : IntoFrameT | None, optional
            Dataframe for column lookup. Default is None.
        columns : str | list | None, optional
            Column names or patterns. Default is None.
        clause : str, optional
            Pre-constructed clause. Default is "".
        case_insensitive : bool, optional
            Case-insensitive matching. Default is False.

        Returns
        -------
        str | FormulaBuilder
            Formula string or self for chaining.
        """
        if self is None:
            caller = FormulaBuilder
        else:
            caller = self

        return caller.scale(
            df=df,
            columns=columns,
            clause=clause,
            case_insensitive=case_insensitive,
            standardize=True,
        )